Title:	Calculating Bilateral and Multilateral Price Indexes
Version:	0.2.3
Description:	Preparing a scanner data set for price dynamics calculations (data selecting, data classification, data matching, data filtering). Computing bilateral and multilateral indexes. For details on these methods see: Diewert and Fox (2020) <doi:10.1080/07350015.2020.1816176>, Białek (2019) <doi:10.2478/jos-2019-0014> or Białek (2020) <doi:10.2478/jos-2020-0037>.
Depends:	R (≥ 3.5.0)
Imports:	lubridate (≥ 1.7.4), dplyr (≥ 0.8.3), ggplot2 (≥ 3.2.0), reshape, reclin2, stringr, xgboost, caret, strex
License:	GPL-3
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.1.1
Suggests:	testthat, knitr, rmarkdown
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-01-24 07:33:12 UTC; User
Author:	Jacek Białek [aut, cre]
Maintainer:	Jacek Białek <jacek.bialek@uni.lodz.pl>
Repository:	CRAN
Date/Publication:	2025-01-24 07:50:02 UTC

Calculating the implicit quadratic mean of order r price index

Description

This function returns a value (or vector of values) of the implicit quadratic mean of order r price index.

Usage

IQMp(data, start, end, r = 2, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the implicit quadratic mean of order r price index - see CPI Manual (2004), Section 17.37, formula 17.32 (page 321).

References

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

IQMp(sugar, start="2019-01", end="2020-01")
IQMp(sugar, start="2019-01", end="2020-01", r=1.3, interval=TRUE)

The list of package functions and their demonstration

Description

The PriceIndices package is a tool for Bilateral and Multilateral Price Index Calculations. A demonstration of package functions is here: README. The package documentation can be found HERE. The list of package functions is as follows:

Data sets in the package and generating artificial scanner data sets

dataAGGR

dataMATCH

dataCOICOP

milk

sugar

coffee

dataU

generate

generate_CES

tindex

Functions for data processing

data_check

data_preparing

data_imputing

data_aggregating

data_unit

data_norm

data_selecting

data_classifying

model_classification

save_model

load_model

data_matching

data_filtering

data_reducing

Functions providing dataset characteristics

available

matched

matched_index

matched_fig

products

products_fig

prices

quantities

sales

sales_groups

sales_groups2

expenditures

pqcor

pqcor_fig

dissimilarity_fig

elasticity

elasticity_fig

Functions for bilateral unweighted price index calculation

bmw

carli

cswd

dutot

jevons

harmonic

Functions for bilateral weighted index calculation

agmean

banajree

bialek

davies

drobisch

fisher

geary_khamis

geolaspeyres

geolowe

geopaasche

geoyoung

geohybrid

hybrid

laspeyres

lehr

lloyd_moulton

lowe

marshall_edgeworth

paasche

palgrave

sato_vartia

stuvel

tornqvist

vartia

walsh

young

QMp

IQMp

QMq

value_index

unit_value_index

Functions for chain index calculation

chbmw

chcarli

chcswd

chdutot

chjevons

chharmonic

chagmean

chbanajree

chbialek

davies

chdrobisch

chfisher

chgeary_khamis

chgeolaspeyres

chgeolowe

chgeopaasche

chgeoyoung

chgeohybrid

chhybrid

chlaspeyres

chlehr

chlloyd_moulton

chlowe

chmarshall_edgeworth

chpaasche

chpalgrave

chsato_vartia

chstuvel

chtornqvist

chvartia

chwalsh

chyoung

chQMp

chIQMp

chQMq

Functions for multilateral price index calculation

ccdi

geks

wgeks

geksl

wgeksl

geksgl

wgeksgl

geksaqu

wgeksaqu

geksaqi

wgeksaqi

geksgaqi

wgeksgaqi

geksj

geksw

geksqm

geksiqm

gekslm

gk

QU

tpd

SPQ

m_decomposition

Functions for extending multilateral price indices by using splicing methods

ccdi_splice

geks_splice

wgeks_splice

geksj_splice

geksw_splice

geksl_splice

wgeksl_splice

geksgl_splice

wgeksgl_splice

geksaqu_splice

wgeksaqu_splice

geksaqi_splice

wgeksaqi_splice

geksgaqi_splice

wgeksgaqi_splice

geksqm_splice

geksiqm_splice

gekslm_splice

gk_splice

tpd_splice

Functions for extending multilateral price indices by using the FBEW method

ccdi_fbew

geks_fbew

wgeks_fbew

geksj_fbew

geksw_fbew

geksl_fbew

wgeksl_fbew

geksgl_fbew

wgeksgl_fbew

geksaqu_fbew

wgeksaqu_fbew

geksaqi_fbew

wgeksaqi_fbew

geksgaqi_fbew

wgeksgaqi_fbew

geksqm_fbew

geksiqm_fbew

gekslm_fbew

gk_fbew

tpd_fbew

Functions for extending multilateral price indices by using the FBMW method

ccdi_fbmw

geks_fbmw

wgeks_fbmw

geksj_fbmw

geksw_fbmw

geksl_fbmw

wgeksl_fbmw

geksgl_fbmw

wgeksgl_fbmw

geksaqu_fbmw

wgeksaqu_fbmw

geksaqi_fbmw

wgeksaqi_fbmw

geksgaqi_fbmw

wgeksgaqi_fbmw

geksqm_fbmw

geksiqm_fbmw

gekslm_fbmw

gk_fbmw

tpd_fbmw

Functions for bilateral indicator calculations

bennet

montgomery

Functions for multilateral indicator calculations

mbennet

mmontgomery

General functions for price index calculations

price_indices

final_index

Functions for comparisons of price indices

compare_indices_df

compare_indices_list

compare_indices_jk

compare_distances

compare_to_target

Calculating the quadratic mean of order r price index

Description

This function returns a value (or vector of values) of the quadratic mean of order r price index.

Usage

QMp(data, start, end, r = 2, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the quadratic mean of order r price index - see CPI Manual (2004), Section 17.40, formula 17.35 (page 321).

References

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

QMp(sugar, start="2019-01", end="2020-01")
QMp(sugar, start="2019-01", end="2020-01", r=1.3, interval=TRUE)

Calculating the quadratic mean of order r quantity index

Description

This function returns a value (or vector of values) of the quadratic mean of order r quantity index.

Usage

QMq(data, start, end, r = 2, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the quadratic mean of order r quantity index - see CPI Manual (2004), Section 17.35, formula 17.30 (page 321).

References

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

QMq(sugar, start="2019-01", end="2020-01")
QMq(sugar, start="2019-01", end="2020-01", r=1.3, interval=TRUE)

Calculating the quality adjusted unit value index (QU index)

Description

This function returns a value of the quality adjusted unit value index (QU index) for a given set of adjustment factors.

Usage

QU(data, start, end, v)

Arguments

Value

This function returns a value of the quality adjusted unit value index (QU index) for a given set of adjustment factors (adjusted factors must be available for all matched prodIDs).

References

Chessa, A.G. (2016). A New Methodology for Processing Scanner Data in the Dutch CPI. Eurona 1/2016, 49-69.

Examples

## Creating a data frame with artificial adjustment factors 
## (random numbers from uniform distribution U[1,2])
prodID<-unique(milk$prodID)
values<-stats::runif(length(prodID),1,2)
v<-data.frame(prodID,values)
## Calculating the QU index for the created data frame 'v'
QU(milk, start="2018-12", end="2019-12", v)

Calculating the multilateral SPQ price index

Description

This function returns a value of the multilateral SPQ price index which is based on the relative price and quantity dissimilarity measure.

Usage

SPQ(data, start, end, interval = FALSE)

Arguments

Value

This function returns a value of the multilateral SPQ price index which is based on the relative price and quantity dissimilarity measure (see References). If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Diewert, E. (2020). The Chain Drift Problem and Multilateral Indexes. Chapter 6 in: Consumer Price Index Theory (draft)

Examples

SPQ(sugar, start="2018-12",end="2019-02")
SPQ(milk, start="2018-12",end="2019-12",interval=TRUE)

Calculating the bilateral AG Mean price index

Description

This function returns a value (or vector of values) of the bilateral AG Mean price index.

Usage

agmean(data, start, end, sigma = 0.7, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the bilateral AG Mean price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Lent J., & Dorfman,A. H. (2009). Using a Weighted Average of Base Period Price Indexes to Approximate a Superlative Index. Journal of Official Statistics, 25(1), 139-149.

Examples

agmean(sugar, start="2019-01", end="2020-01",sigma=0.5)
agmean(milk, start="2018-12", end="2020-01", interval=TRUE)

Providing values from the indicated column that occur at least once in one of the compared periods or in a given time interval

Description

The function returns all values from the indicated column (defined by the type parameter) which occur at least once in one of the compared periods or in a given time interval.

Usage

available(data, period1, period2, type = "prodID", interval = FALSE)

Arguments

Value

The function returns all values from the indicated column (defined by the type parameter) which occur at least once in one of the compared periods or in a given time interval. Possible values of the type parameter are: retID, prodID, codeIN, codeOUT or description. If the interval parameter is set to FALSE, then the function compares only periods defined by period1 and period2. Otherwise the whole time period between period1 and period2 is considered.

Examples

available(milk, period1="2018-12", period2="2019-12", interval=TRUE)
available(milk, period1="2018-12", period2="2019-12", type="description")

Calculating the bilateral Banajree price index

Description

This function returns a value (or vector of values) of the bilateral Banajree price index.

Usage

banajree(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the bilateral Banajree price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function)..

References

Banajree, K. S. (1977). On the factorial approach providing the true index of cost of living. Gottingen : Vandenhoeck und Ruprecht.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Von der Lippe, P. (2007). Index Theory and Price Statistics. Peter Lang: Berlin, Germany.

Examples

banajree(sugar, start="2018-12", end="2019-12")
banajree(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the Bennet price and quantity indicators

Description

This function returns the Bennet price and quantity indicators and optionally also the price and quantity contributions of individual products.

Usage

bennet(
  data,
  start,
  end,
  interval = FALSE,
  matched = FALSE,
  contributions = FALSE,
  prec = 2
)

Arguments

Value

This function returns the Bennet price and quantity indicators and optionally also the price and quantity contributions of individual products.

References

Bennet, T. L. (1920). The Theory of Measurement of Changes in Cost of Living. Journal of the Royal Statistical Society, 83, 455-462.

Białek, J. (2024). The use of the Bennet indicators and their transitive versions for scanner data analysis. Statistics in Transition new series, 25(3), 155-173.

Examples

bennet(milk, "2018-12", "2019-12", matched=TRUE, contributions=TRUE)
bennet(coffee, start="2018-12", end="2019-03", interval=TRUE)

Calculating the bilateral Bialek price index

Description

This function returns a value (or vector of values) of the bilateral Bialek price index.

Usage

bialek(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the bilateral Bialek price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Von der Lippe, P. (2012). Some short notes on the price index of Jacek Bialek. Econometrics (Ekonometria). 1(35), 76-83.

Bialek, J. (2013). Some Remarks on the Original Price Index Inspired by the Notes of Peter von der Lippe. Econometrics (Ekonometria), 3(41), 40-54.

Bialek, J. (2014). Simulation Study of an Original Price Index Formula. Communications in Statistics - Simulation and Computation, 43(2), 285-297

Examples

bialek(sugar, start="2018-12", end="2019-12")
bialek(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the unweighted BMW price index

Description

This function returns a value (or vector of values) of the unweighted Balk-Mehrhoff-Walsh (BMW) price index.

Usage

bmw(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the unweighted bilateral BMW price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Mehrhoff, J.(2007). A linear approximation to the Jevons index. In: Von der Lippe (2007): Index Theory and Price Statistics, Peter Lang: Berlin, Germany.

(2018). Harmonised Index of Consumer Prices (HICP). Methodological Manual. Publication Office of the European union, Luxembourg.

Examples

bmw(sugar, start="2018-12", end="2019-12")
bmw(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the unweighted Carli price index

Description

This function returns a value (or vector of values) of the unweighted bilateral Carli price index.

Usage

carli(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the unweighted bilateral Carli price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Carli, G. (1804). Del valore e della proporzione de'metalli monetati. Scrittori Classici Italiani di Economia Politica, 13, 297-336.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

carli(sugar, start="2018-12", end="2019-12")
carli(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the multilateral GEKS price index based on the Tornqvist formula (typical notation: GEKS-T or CCDI)

Description

This function returns a value of the multilateral CCDI price index, i.e. the GEKS price index based on the superlative Tornqvist index formula.

Usage

ccdi(data, start, end, wstart = start, window = 13)

Arguments

Value

This function returns a value of the multilateral CCDI price index (to be more precise: the GEKS index based on the Tornqvist formula) which considers the time window defined by wstart and window parameters. It measures the price dynamics by comparing period end to period start (both start and end must be inside the considered time window). To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Gini, C. (1931). On the Circular Test of Index Numbers. Metron 9:9, 3-24.

Elteto, O., and Koves, P. (1964). On a Problem of Index Number Computation Relating to International Comparisons. Statisztikai Szemle 42, 507-518.

Szulc, B. (1983). Linking Price Index Numbers. In: Price Level Measurement, W. E. Diewert and C. Montmarquette (eds.), 537-566.

Caves, D.W., Christensen, L.R. and Diewert, W.E. (1982). Multilateral comparisons of output, input, and productivity using superlative index numbers. Economic Journal 92, 73-86.

Examples

ccdi(milk, start="2019-01", end="2019-08",window=10)
ccdi(milk, start="2018-12", end="2019-12")

Extending the multilateral CCDI price index by using the FBEW method.

Description

This function returns a value of the multilateral CCDI price index (GEKS based on the Tornqvist formula) extended by using the FBEW (Fixed Base Monthly Expanding Window) method.

Usage

ccdi_fbew(data, start, end)

Arguments

Value

This function returns a value of the multilateral CCDI price index extended by using the FBEW (Fixed Base Monthly Expanding Window) method. The FBEW method uses a time window with a fixed base month every year (December). The window is enlarged every month with one month in order to include information from a new month. The full window length (13 months) is reached in December of each year. The function measures the price dynamics between periods end and start. The month of the start parameter must be December. If the distance between end and start exceeds 13 months, then internal Decembers play a role of chain-linking months. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Caves, D.W., Christensen, L.R. and Diewert, W.E. (1982). Multilateral comparisons of output, input, and productivity using superlative index numbers. Economic Journal 92, 73-86.

Chessa, A.G. (2016). A New Methodology for Processing Scanner Data in the Dutch CPI. Eurona 1/2016, 49-69.

Examples

ccdi_fbew(milk, start="2018-12", end="2019-08")

Extending the multilateral CCDI price index by using the FBMW method.

Description

This function returns a value of the multilateral CCDI price index (GEKS based on the Tornqvist formula) extended by using the FBMW (Fixed Base Moving Window) method.

Usage

ccdi_fbmw(data, start, end)

Arguments

Value

This function returns a value of the multilateral CCDI price index extended by using the FBMW (Fixed Base Moving Window) method. It measures the price dynamics between periods end and start and it uses a 13-month time window with a fixed base month taken as year(end)-1. If the distance between end and start exceeds 13 months, then internal Decembers play a role of chain-linking months. The month of the start parameter must be December. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Caves, D.W., Christensen, L.R. and Diewert, W.E. (1982). Multilateral comparisons of output, input, and productivity using superlative index numbers. Economic Journal 92, 73-86.

Lamboray, C.(2017). The Geary Khamis index and the Lehr index: how much do they differ? Paper presented at the 15th Ottawa Group meeting, 10-12 May 2017, Elville am Rhein, Germany.

Examples

ccdi_fbmw(milk, start="2019-12", end="2020-04")

Extending the multilateral CCDI price index by using window splicing methods.

Description

This function returns a value (or values) of the multilateral CCDI price index (GEKS based on the Tornqvist formula) extended by using window splicing methods. Available splicing methods are: movement splice, window splice, half splice, mean splice and their additional variants: window splice on published indices (WISP), half splice on published indices (HASP) and mean splice on published indices (see References).

Usage

ccdi_splice(
  data,
  start,
  end,
  window = 13,
  splice = "movement",
  interval = FALSE
)

Arguments

Value

This function returns a value or values (depending on interval parameter) of the multilateral CCDI price index extended by using window splicing methods. Available splicing methods are: movement splice, window splice, half splice, mean splice and their additional variants: window splice on published indices (WISP), half splice on published indices (HASP) and mean splice on published indices (see References). The time window starts in start and should consist of at least two months. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Caves, D.W., Christensen, L.R. and Diewert, W.E. (1982). Multilateral comparisons of output, input, and productivity using superlative index numbers. Economic Journal 92, 73-86.

de Haan, J., van der Grient, H.A. (2011). Eliminating chain drift in price indexes based on scanner data. Journal of Econometrics, 161, 36-46.

Krsinich, F. (2014). The FEWS Index: Fixed Effects with a Window Splice? Non-Revisable Quality-Adjusted Price Indices with No Characteristic Information. Paper presented at the UNECE-ILO Meeting of the Group of Experts on Consumer Price Indices, 2-4 May 2016, Geneva, Switzerland.

de Haan, J.(2015). A Framework for Large Scale Use of Scanner Data in the Dutch CPI. Paper presented at the 14th Ottawa Group meeting, Tokyo, Japan.

Diewert, W.E., and Fox, K.J. (2017). Substitution Bias in Multilateral Methods for CPI Construction using Scanner Data. Discussion paper 17-02, Vancouver School of Economics, The University of British Columbia, Vancouver, Canada.

Examples

ccdi_splice(milk, start="2018-12", end="2020-02",splice="half")

Calculating the monthly chained implicit quadratic mean of order r price index

Description

This function returns a value (or vector of values) of the monthly chained implicit quadratic mean of order r price index.

Usage

chIQMp(data, start, end, r = 2, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained implicit quadratic mean of order r price index - see CPI Manual (2004), Section 17.37, formula 17.32 (page 321).

References

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

chIQMp(sugar, start="2019-01", end="2020-01")
chIQMp(sugar, start="2019-01", end="2020-01", r=1.3, interval=TRUE)

Calculating the monthly chained quadratic mean of order r price index

Description

This function returns a value (or vector of values) of the monthly chained quadratic mean of order r price index.

Usage

chQMp(data, start, end, r = 2, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained quadratic mean of order r price index - see CPI Manual (2004), Section 17.40, formula 17.35 (page 321).

References

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

chQMp(sugar, start="2019-01", end="2020-01")
chQMp(sugar, start="2019-01", end="2020-01", r=1.3, interval=TRUE)

Calculating the monthly chained quadratic mean of order r quantity index

Description

This function returns a value (or vector of values) of the monthly chained quadratic mean of order r quantity index.

Usage

chQMq(data, start, end, r = 2, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained quadratic mean of order r quantity index - see CPI Manual (2004), Section 17.35, formula 17.30 (page 321).

References

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

chQMq(sugar, start="2019-01", end="2020-01")
chQMq(sugar, start="2019-01", end="2020-01", r=1.3, interval=TRUE)

Calculating the monthly chained AG Mean price index

Description

This function returns a value (or vector of values) of the monthly chained AG Mean price index.

Usage

chagmean(data, start, end, sigma = 0.7, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained AG Mean price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Lent J., & Dorfman,A. H. (2009). Using a Weighted Average of Base Period Price Indexes to Approximate a Superlative Index. Journal of Official Statistics, 25(1), 139-149.

Examples

chagmean(sugar, start="2019-01", end="2019-04",sigma=0.5)
chagmean(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the monthly chained Banajree price index

Description

This function returns a value (or vector of values) of the monthly chained Banajree price index.

Usage

chbanajree(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained Banajree price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Banajree, K. S. (1977). On the factorial approach providing the true index of cost of living. Gottingen : Vandenhoeck und Ruprecht.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Von der Lippe, P. (2007). Index Theory and Price Statistics. Peter Lang: Berlin, Germany.

Examples

chbanajree(sugar, start="2018-12", end="2019-04")
chbanajree(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the monthly chained Bialek price index

Description

This function returns a value (or vector of values) of the monthly chained Bialek price index.

Usage

chbialek(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained Bialek price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Von der Lippe, P. (2012). Some short notes on the price index of Jacek Bialek. Econometrics (Ekonometria). 1(35), 76-83.

Bialek, J. (2013). Some Remarks on the Original Price Index Inspired by the Notes of Peter von der Lippe. Econometrics (Ekonometria), 3(41), 40-54.

Bialek, J. (2014). Simulation Study of an Original Price Index Formula. Communications in Statistics - Simulation and Computation, 43(2), 285-297

Examples

chbialek(sugar, start="2018-12", end="2019-04")
chbialek(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the monthly chained BMW price index

Description

This function returns a value (or vector of values) of the monthly chained Balk-Mehrhoff-Walsh (BMW) price index.

Usage

chbmw(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained BMW price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Mehrhoff, J.(2007). A linear approximation to the Jevons index. In: Von der Lippe (2007): Index Theory and Price Statistics, Peter Lang: Berlin, Germany.

(2018). Harmonised Index of Consumer Prices (HICP). Methodological Manual. Publication Office of the European union, Luxembourg.

Examples

chbmw(sugar, start="2018-12", end="2019-04")
chbmw(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the monthly chained Carli price index

Description

This function returns a value (or vector of values) of the monthly chained Carli price index.

Usage

chcarli(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained Carli price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Carli, G. (1804). Del valore e della proporzione de'metalli monetati. Scrittori Classici Italiani di Economia Politica, 13, 297-336.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

chcarli(sugar, start="2018-12", end="2019-04")
chcarli(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the monthly chained CSWD price index

Description

This function returns a value (or vector of values) of the monthly chained Carruthers-Sellwood-Ward-Dalen (CSWD) price index.

Usage

chcswd(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained CSWD price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Carruthers, A.G., Sellwood, D. J, Ward, P. W. (1980). Recent developments in the retail price index. Journal of the Royal Statistical Society. Series D (The Statisticain), 29(1), 1-32.

Dalen, J. (1992). Recent developments in the retail price index. The Statistician, 29(1), 1-32.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

chcswd(sugar, start="2018-12", end="2019-04")
chcswd(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the monthly chained Davies price index

Description

This function returns a value (or vector of values) of the monthly chained Davies price index.

Usage

chdavies(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained Davies price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Davies, G. R. (1924). The Problem of a Standard Index Number Formula. Journal of the American Statistical Association, 19 (146), 180-188.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Von der Lippe, P. (2007). Index Theory and Price Statistics. Peter Lang: Berlin, Germany.

Examples

chdavies(sugar, start="2018-12", end="2019-04")
chdavies(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the monthly chained Dikhanov price index

Description

This function returns a value (or vector of values) of the monthly chained Dikhanov price index.

Usage

chdikhanov(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained Dikhanov price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Dikhanov, Y., (2024). A New Elementary Index Number. Paper presented at the 18th Meeting of the Ottawa Group on Price Indices, Ottawa, Canada.

Examples

chdikhanov(sugar, start="2018-12", end="2019-04")
chdikhanov(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the monthly chained Drobisch price index

Description

This function returns a value (or vector of values) of the monthly chained Drobisch price index.

Usage

chdrobisch(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained Drobisch price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Drobisch, M. W. (1871). Ueber einige Einwurfe gegen die in diesen Jahrbuchern veroffentlichte neue Methode, die Veranderungen der Waarenpreise und des Geldwerths zu berechten.Jahrbucher fur Nationalokonomie und Statistik, Vol. 16, s. 416-427.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Von der Lippe, P. (2007). Index Theory and Price Statistics. Peter Lang: Berlin, Germany.

Examples

chdrobisch(sugar, start="2018-12", end="2019-04")
chdrobisch(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the monthly chained Dutot price index

Description

This function returns a value (or vector of values) of the monthly chained Dutot price index.

Usage

chdutot(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained Dutot price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Dutot, C. F., (1738). Reflexions Politiques sur les Finances et le Commerce. The Hague: Les Freres Vaillant et Nicolas Prevost, Vol. 1.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

chdutot(sugar, start="2018-12", end="2019-04")
chdutot(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the monthly chained Fisher price index

Description

This function returns a value (or vector of values) of the monthly chained Fisher price index.

Usage

chfisher(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained Fisher price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Fisher, I. (1922). The Making of Index Numbers. Boston: Houghton Mifflin.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

chfisher(sugar, start="2018-12", end="2019-04")
chfisher(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the monthly chained Geary-Khamis price index

Description

This function returns a value (or vector of values) of the monthly chained Geary-Khamis price index.

Usage

chgeary_khamis(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained Geary-Khamis price index depending on the interval parameter (please use gk function to calculate the multilateral Geary-Khamis price index). If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (see the final_index function).

References

Geary, R. G. (1958). A Note on Comparisons of Exchange Rates and Purchasing Power between Countries. Journal of the Royal Statistical Society, Series A, 121, 97-99.

Khamis, S. H. (1970). Properties and Conditions for the Existence of a new Type of Index Number. Sankhya Series B32, 81-98.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Von der Lippe, P. (2007). Index Theory and Price Statistics. Peter Lang: Berlin, Germany.

Examples

chgeary_khamis(sugar, start="2018-12", end="2019-04")
chgeary_khamis(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the the monthly chained geohybrid price index

Description

This function returns a value (or vector of values) of the monthly chained geohybrid price index. The geohybrid index was proposed by Bialek (2020) and it uses correlation coefficients between prices and quantities.

Usage

chgeohybrid(data, start, end, base = start, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained geohybrid price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Bialek, J. (2020). Proposition of a Hybrid Price Index Formula for the Consumer Price Index Measurement. Equilibrium. Quarterly Journal of Economics and Economic Policy, 15(4), 697-716.

Examples

chgeohybrid(sugar, start="2019-12", end="2020-05", base="2018-12")
chgeohybrid(milk, start="2019-12", end="2020-08", base="2018-12", interval=TRUE)

Calculating the monthly chained geo-logarithmic Laspeyres price index

Description

This function returns a value (or vector of values) of the monthly chained geo-logarithmic Laspeyres price index.

Usage

chgeolaspeyres(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained geo-logarithmic Laspeyres price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Von der Lippe, P. (2007). Index Theory and Price Statistics. Peter Lang: Berlin, Germany.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

chgeolaspeyres(sugar, start="2018-12", end="2019-04")
chgeolaspeyres(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the monthly chained geometric Lowe price index

Description

This function returns a value (or vector of values) of the monthly chained geometric Lowe price index.

Usage

chgeolowe(data, start, end, base = start, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained geometric Lowe price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

chgeolowe(sugar, start="2019-01", end="2019-04",base="2018-12")
chgeolowe(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the monthly chained geo-logarithmic Paasche price index

Description

This function returns a value (or vector of values) of the monthly chained geo-logarithmic Paasche price index.

Usage

chgeopaasche(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained geo-logarithmic Paasche price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Von der Lippe, P. (2007). Index Theory and Price Statistics. Peter Lang: Berlin, Germany.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

chgeopaasche(sugar, start="2018-12", end="2019-04")
chgeopaasche(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the monthly chained geometric Young price index

Description

This function returns a value (or vector of values) of the monthly chained geometric Young price index.

Usage

chgeoyoung(data, start, end, base = start, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained geometric Young price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Young, A. H. (1992). Alternative Measures of Change in Real Output and Prices. Survey of Current Business, 72, 32-48.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

chgeoyoung(sugar, start="2019-01", end="2019-04",base="2018-12")
chgeoyoung(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the monthly chained harmonic price index

Description

This function returns a value (or vector of values) of the monthly chained "unnamed" harmonic price index.

Usage

chharmonic(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained harmonic price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Von der Lippe, P. (2007). Index Theory and Price Statistics. Peter Lang: Berlin, Germany.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

chharmonic(sugar, start="2018-12", end="2019-04")
chharmonic(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the the monthly chained hybrid price index

Description

This function returns a value (or vector of values) of the monthly chained hybrid price index. The hybrid index was proposed by Bialek (2020) and it uses correlation coefficients between prices and quantities.

Usage

chhybrid(data, start, end, base = start, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained hybrid price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Bialek, J. (2020). Proposition of a Hybrid Price Index Formula for the Consumer Price Index Measurement. Equilibrium. Quarterly Journal of Economics and Economic Policy, 15(4), 697-716.

Examples

chhybrid(sugar, start="2019-12", end="2020-05", base="2018-12")
chhybrid(milk, start="2019-12", end="2020-08", base="2018-12", interval=TRUE)

Calculating the monthly chained Jevons price index

Description

This function returns a value (or vector of values) of the monthly chained Jevons price index

Usage

chjevons(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained Jevons price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Jevons, W. S., (1865). The variation of prices and the value of the currency since 1782. J. Statist. Soc. Lond., 28, 294-320.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

chjevons(sugar, start="2018-12", end="2019-04")
chjevons(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the monthly chained Laspeyres price index

Description

This function returns a value (or vector of values) of the monthly chained Laspeyres price index.

Usage

chlaspeyres(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained Laspeyres price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Laspeyres, E. (1871). Die Berechnung einer mittleren Waarenpreissteigerung. Jahrbucher fur Nationalokonomie und Statistik 16, 296-314.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

chlaspeyres(sugar, start="2018-12", end="2019-04")
chlaspeyres(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the monthly chained Lehr price index

Description

This function returns a value (or vector of values) of the monthly chained Lehr price index.

Usage

chlehr(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained Lehr price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Lehr, J. (1885). Beitrage zur Statistik der Preise, insbesondere des Geldes und des Holzes. J. D. Sauerlander, Frankfurt am Main.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

chlehr(sugar, start="2018-12", end="2019-04")
chlehr(milk, start="2018-12", end="2020-01", TRUE)

Calculating the monthly chained Lloyd-Moulton price index

Description

This function returns a value (or vector of values) of the monthly chained Lloyd-Moulton price index.

Usage

chlloyd_moulton(data, start, end, sigma = 0.7, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained Lloyd-Moulton price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Lloyd, P. J. (1975). Substitution Effects and Biases in Nontrue Price Indices. The American Economic Review, 65, 301-313.

Moulton, B. R. (1996). Constant Elasticity Cost-of-Living Index in Share-Relative Form. Washington DC: U. S. Bureau of Labor Statistics, mimeograph

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Von der Lippe, P. (2007). Index Theory and Price Statistics. Peter Lang: Berlin, Germany.

Examples

chlloyd_moulton(sugar, start="2018-12", end="2019-04",sigma=0.9)
chlloyd_moulton(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the monthly chained Lowe price index

Description

This function returns a value (or vector of values) of the monthly chained Lowe price index.

Usage

chlowe(data, start, end, base = start, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained Lowe price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

chlowe(sugar, start="2019-01", end="2019-04",base="2018-12")
chlowe(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the monthly chained Marshall-Edgeworth price index

Description

This function returns a value (or vector of values) of the monthly chained Marshall-Edgeworth price index.

Usage

chmarshall_edgeworth(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained Marshall-Edgeworth price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Marshall, A. (1887). Remedies for Fluctuations of General Prices. Contemporary Review, 51, 355-375.

Edgeworth, F. Y. (1887). Measurement of Change in Value of Money I. The first Memorandum presented to the British Association for the Advancement of Science; reprinted in Papers Relating to Political Economy, Vol. 1, New York, Burt Franklin, s. 1925.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Von der Lippe, P. (2007). Index Theory and Price Statistics. Peter Lang: Berlin, Germany.

Examples

chmarshall_edgeworth(sugar, start="2018-12", end="2019-04")
chmarshall_edgeworth(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the monthly chained Paasche price index

Description

This function returns a value (or vector of values) of the monthly chained Paasche price index.

Usage

chpaasche(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained Paasche price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Paasche, H. (1874). Uber die Preisentwicklung der letzten Jahre nach den Hamburger Borsennotirungen. Jahrbucher fur Nationalokonomie und Statistik, 12, 168-178.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

chpaasche(sugar, start="2018-12", end="2019-04")
chpaasche(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the monthly chained Palgrave price index

Description

This function returns a value (or vector of values) of the monthly chained Palgrave price index.

Usage

chpalgrave(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained Palgrave price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Palgrave, R. H. I. (1886). Currency and Standard of Value in England, France and India and the Rates of Exchange Between these Countries. Memorandum submitted to the Royal Commission on Depression of trade and Industry, Third Report, Appendix B, 312-390.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Von der Lippe, P. (2007). Index Theory and Price Statistics. Peter Lang: Berlin, Germany.

Examples

chpalgrave(sugar, start="2018-12", end="2019-04")
chpalgrave(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the monthly chained Vartia-II (Sato-Vartia) price index

Description

This function returns a value (or vector of values) of the monthly chained Vartia-II (Sato-Vartia) price index.

Usage

chsato_vartia(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained Vartia-II (Sato-Vartia) price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Sato, K. (1976). The Ideal Log-Change Index Number. The Review of Economics and Statistics, 58(2), 223-228.

Vartia, Y. 0. (1976). Ideal Log-Change Index Numbers . Scandinavian Journal of Statistics 3(3), 121-126.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Von der Lippe, P. (2007). Index Theory and Price Statistics. Peter Lang: Berlin, Germany.

Examples

chsato_vartia(sugar, start="2018-12", end="2019-04")
chsato_vartia(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the monthly chained Stuvel price index

Description

This function returns a value (or vector of values) of the monthly chained Stuvel price index.

Usage

chstuvel(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained Stuvel price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Stuvel, G. (1957). A New Index Number Formula. Econometrica, 25, 123-131.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Von der Lippe, P. (2007). Index Theory and Price Statistics. Peter Lang: Berlin, Germany.

Examples

chstuvel(sugar, start="2018-12", end="2019-04")
chstuvel(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the monthly chained Tornqvist price index

Description

This function returns a value (or vector of values) of the monthly chained Tornqvist price index.

Usage

chtornqvist(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained Tornqvist price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Tornqvist, L. (1936). The Bank of Finland's Consumption Price Index. Bank of Finland Monthly Bulletin 10, 1-8.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

chtornqvist(sugar, start="2018-12", end="2019-04")
chtornqvist(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the monthly chained Vartia-I price index

Description

This function returns a value (or vector of values) of the monthly chained Vartia-I price index.

Usage

chvartia(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained Vartia-I price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Vartia, Y. 0. (1976). Ideal Log-Change Index Numbers . Scandinavian Journal of Statistics 3(3), 121-126.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Von der Lippe, P. (2007). Index Theory and Price Statistics. Peter Lang: Berlin, Germany.

Examples

chvartia(sugar, start="2018-12", end="2019-04")
chvartia(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the monthly chained Walsh price index

Description

This function returns a value (or vector of values) of the monthly chained Walsh price index.

Usage

chwalsh(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained Walsh price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Walsh, C. M. (1901). The Measurement of General Exchange Value. The MacMillan Company, New York.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Von der Lippe, P. (2007). Index Theory and Price Statistics. Peter Lang: Berlin, Germany.

Examples

chwalsh(sugar, start="2018-12", end="2019-04")
chwalsh(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the monthly chained Young price index

Description

This function returns a value (or vector of values) of the monthly chained Young price index.

Usage

chyoung(data, start, end, base = start, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the monthly chained Young price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Young, A. H. (1992). Alternative Measures of Change in Real Output and Prices. Survey of Current Business, 72, 32-48.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

chyoung(sugar, start="2019-01", end="2019-04",base="2018-12")
chyoung(milk, start="2018-12", end="2020-01", interval=TRUE)

A real data set on sold coffee

Description

A collection of scanner data on the sale of coffee in one of Polish supermarkets in the period from December 2017 to October 2020

Usage

coffee

Format

A data frame with 6 columns and 42561 rows. The used variables are as follows:

time - Dates of transactions (Year-Month-Day)

prices - Prices of sold products [PLN]

quantities - Quantities of sold products [kg]

prodID - Unique product codes (data set contains 79 different prodIDs)

retID - Unique codes identifying outlets/retailer sale points (data set contains 20 different retIDs)

description Descriptions of sold coffee products (data set contains 3 different product descriptions)

Calculating distances between price indices

Description

The function calculates distances between price indices

Usage

compare_distances(
  data = data.frame(),
  measure = "MAD",
  pp = TRUE,
  first = FALSE,
  prec = 3
)

Arguments

Value

The function calculates average distances between price indices and it returns a data frame with these values for each pair of price indices.

Examples

#Creating a data frame with unweighted bilateral index values
df<-price_indices(milk, 
formula=c("jevons","dutot","carli"), 
start="2018-12", end="2019-12",interval=TRUE)
#Calculating average distances between indices (in p.p)
compare_distances(df)

A function for graphical comparison of price indices

Description

This function returns a figure with plots of selected price indices.

Usage

compare_indices_df(
  data,
  names = colnames(data)[2:length(colnames(data))],
  date_breaks = "1 month"
)

Arguments

Value

This function returns a figure with plots of previously calculated indices (together with dates on X-axis and a corresponding legend). Indices must be provided as a data frame, where the the first column must includes dates limited to the year and month (e.g.: "2020-04").

Examples

df<-price_indices(milk, start = "2018-12", end = "2019-12", 
formula=c("laspeyres", "fisher"), interval = TRUE)
compare_indices_df(df)

A general function to compare indices by using the jackknife method

Description

This function presents a comparison of selected indices obtained by using the jackknife method.

Usage

compare_indices_jk(
  data,
  start,
  end,
  by = "prodID",
  formula = c(),
  window = c(),
  splice = c(),
  base = c(),
  sigma = c(),
  r = c(),
  names = c(),
  title_iterations = c(),
  title_pseudovalues = c()
)

Arguments

Value

This function presents a comparison of selected indices obtained by using the jackknife method. In particular, it returns a list with four elements: iterations, which is a data frame with basic characteristics of the calculated iteration index values (means, standard deviations, coefficients of variation and results for all sample), pseudovalues, which is a data frame with basic characteristics of the calculated index pseudovalues obtained in the jackknife procedure (i.e. the jackknife estimators and their standard deviations and coefficients of variation), figure_iterations which presents a box-plot for the calculated iteration index values, and figure_pseudovalues which presents a box-plot for the calculated index pseudovalues obtained in the jackknife procedure.

References

Quenouille, M.H. (1956). Notes on bias in estimation. Biometrika, 43 (3–4), 353–360

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

milk.<-dplyr::filter(milk, milk$prodID %in% 
sample(unique(milk$prodID),4))
#creating a list with jackknife results
comparison<-compare_indices_jk(milk.,
formula=c("jevons","fisher"),
start="2018-12",
end="2019-12", 
names=c("Jevons","Fisher"), 
title_iterations="Box-plots for iteration values (milk products)",
title_pseudovalues="Box-plots for pseudovalues (milk products)")
#displaying results
comparison$iterations
comparison$pseudovalues
comparison$figure_iterations
comparison$figure_pseudovalues

A general function for graphical comparison of price indices

Description

This function returns a figure with plots of previously calculated price indices.

Usage

compare_indices_list(data = list(), names = c(), date_breaks = "1 month")

Arguments

Value

This function returns a figure with plots of previously calculated price indices. It allows for graphical comparison of price index values which were previously calculated and now are provided as a list of data frames (see data parameter).

Examples

## Caluclating two indices by using two different package functions:
index1<-final_index(data=milk, start="2018-12", 
end="2019-12",formula="walsh",interval=TRUE)
index2<-price_indices(milk,start="2018-12", end="2019-12",
formula="geks",window=13,interval=TRUE)
## Graphical comparison of these two indices 
compare_indices_list(data=list(index1,index2), 
names=c("Walsh index", "GEKS index"))

Calculating distances between considered price indices and the target price index

Description

The function calculates distances between considered price indices and the target price index

Usage

compare_to_target(
  data = data.frame(),
  target,
  measure = "MAD",
  pp = TRUE,
  first = FALSE,
  prec = 3
)

Arguments

Value

The function calculates average distances between considered price indices and the target price index and it returns a data frame with: average distances on the basis of all values of compared indices ('distance' column), average semi-distances on the basis of values of compared indices which overestimate the target index values ('distance_upper' column) and average semi-distances on the basis of values of compared indices which underestimate the target index values ('distance_lower' column).

Examples

#Creating a data frame with example bilateral indices
df<-price_indices(milk, 
formula=c("jevons","laspeyres","paasche","walsh"),
start="2018-12",end="2019-12",interval=TRUE)
#Calculating the target Fisher price index
target_index<-fisher(milk,start="2018-12",end="2019-12",interval=TRUE)
#Calculating average distances between considered indices and the Fisher index (in p.p)
compare_to_target(df,target=target_index)

Calculating the unweighted CSWD price index

Description

This function returns a value (or vector of values) of the unweighted Carruthers-Sellwood-Ward-Dalen (CSWD) price index.

Usage

cswd(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the unweighted bilateral CSWD price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Carruthers, A.G., Sellwood, D. J, Ward, P. W. (1980). Recent developments in the retail price index. Journal of the Royal Statistical Society. Series D (The Statisticain), 29(1), 1-32.

Dalen, J. (1992). Recent developments in the retail price index. The Statistician, 29(1), 1-32.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

cswd(sugar, start="2018-12", end="2019-12")
cswd(milk, start="2018-12", end="2020-01", interval=TRUE)

A small artificial scanner data set for a demonstration of data aggregation

Description

A collection of artificial scanner data on milk products sold in three different months

Usage

dataAGGR

Format

A data frame with 6 columns and 9 rows. The used variables are as follows:

time - Dates of transactions (Year-Month-Day: 4 different dates)

prices - Prices of sold products [PLN]

quantities - Quantities of sold products [l]

prodID - Retailer product codes (3 prodIDs)

retID - Unique codes identifying outlets/retailer sale points (4 retIDs)

description Descriptions of sold products (two subgroups: goat milk, powdered milk)

A real scanner data set for the product classification

Description

A collection of real scanner data on the sale of milk products sold in a period: Dec, 2020 - Feb, 2022.

Usage

dataCOICOP

Format

A data frame with 10 columns and 139600 rows. The used variables are as follows:

time - Dates of transactions (Year-Month-Day)

prices - Prices of sold products [PLN]

quantities - Quantities of sold products

description - Descriptions of sold products (original: in Polish)

codeIN - Retailer product codes

retID - Unique codes identifying outlets/retailer sale points

grammage - Product grammages

unit - Sales units, e.g.: kg, ml, etc.

category - Product categories (in English) corresponding to COICOP 6 levels

coicop6 - Identifiers of local COICOP 6 groups (6 groups)

An artificial scanner data set for product matching

Description

A collection of scanner data on the sale of sample artificial products.

Usage

dataMATCH

Format

A data frame with 7 columns and 30 rows. The used variables are as follows:

time - Dates of transactions (Year-Month-Day)

prices - Prices of sold products [PLN]

quantities - Quantities of sold products [liters]

codeIN - Unique internal (retailer) product codes (data set contains 5 different codeINs)

codeOUT - Unique external product codes (data set contains 5 different codeOUTs)

retID - Unique codes identifying outlets/retailer sale points (data set contains 2 different retIDs)

description Descriptions of sold products (data set contains 3 different product descriptions)

An artificial, small scanner data set

Description

A collection of artificial scanner data on 6 products sold in Dec, 2018. Product descriptions contain the information about their grammage and unit.

Usage

dataU

Format

A data frame with 5 columns and 6 rows. The used variables are as follows:

time - Dates of transactions (Year-Month-Day)

prices - Prices of sold products [PLN]

quantities - Quantities of sold products [item]

prodID - Unique product codes

description Descriptions of sold products (data set contains 6 different product descriptions)

An artificial data set on sold coffee

Description

A collection of scanner data on the sale of coffee in the period from January 2024 to February 2024

Usage

data_DOWN_UP_SIZED

Format

A data frame with 6 columns and 51 rows. The used variables are as follows:

time - Dates of transactions (Year-Month-Day)

prices - Prices of sold products [PLN]

quantities - Quantities of sold products [liters]

codeIN - Unique internal product codes (retaler product codes)

codeOUT - Unique external product codes (e.g. GTIN, EAN, SKU)

description - Descriptions of sold coffee products

Aggregating the user's data frame

Description

The function aggregates the user's data frame over time and optionally over outlets.

Usage

data_aggregating(data, join_outlets = TRUE)

Arguments

Value

The function aggregates the user's data frame over time and/or over outlets. Consequently, we obtain monthly data, where the unit value is calculated instead of a price for each prodID observed in each month (the time column gets the Date format: "Year-Month-01"). If the parameter join_outlets is TRUE, then the function also performs aggregation over outlets (retIDs) and the retID column is removed from the data frame. The main advantage of using this function is the ability to reduce the size of the data frame and the time needed to calculate the price index. Please note, that unnecessary columns are removed (e.g. description).

Examples

#Example 1
data_aggregating(dataAGGR,join_outlets = FALSE)
data_aggregating(dataAGGR,join_outlets = TRUE)
#Example 2 (data frame reduction)
nrow(milk)
nrow(data_aggregating(milk))

Checking the user's data frame

Description

The function checks if the argument data points to a data frame which is suitable for further price index calculation. In particular, the function checks whether the indicated data frame contains the required columns and whether they are of the appropriate type (if not, the function returns FALSE and an appropriate comment).

Usage

data_check(data)

Arguments

Value

The function returns TRUE if the data frame indicated by the data parameter is suitable for the calculation of price indices and returns FALSE otherwise.

Examples

data_check(milk)
data_check(iris)

Predicting product classes via the machine learning model

Description

This function predicts product class levels via the selected machine learning model.

Usage

data_classifying(model = list(), data)

Arguments

Value

This function provides the indicated data set with an additional column, i.e. class_predicted, which is obtained by using the selected machine learning model.

Examples

#Building the model
my.grid=list(eta=c(0.01,0.02,0.05),subsample=c(0.5,0.8))
data_train<-dplyr::filter(dataCOICOP,dataCOICOP$time<=as.Date("2021-10-01"))
data_test<-dplyr::filter(dataCOICOP,dataCOICOP$time==as.Date("2021-11-01"))
ML<-model_classification(data_train,data_test,class="coicop6",grid=my.grid,
indicators=c("description","codeIN", "grammage"),key_words=c("uht"),rounds=60)
#Data classification
data_classifying(ML, data_test)

Filtering a data set for further price index calculations

Description

This function returns a filtered data set, i.e. a reduced user's data frame with the same columns and rows limited by a criterion defined by filters.

Usage

data_filtering(
  data,
  start,
  end,
  filters = c(),
  plimits = c(),
  pquantiles = c(),
  dplimits = c(),
  lambda = 1.25,
  interval = FALSE,
  retailers = FALSE
)

Arguments

Value

This function returns a filtered data set (a reduced user's data frame). If the set of filters is empty, then the function returns the original data frame (defined by the data parameter) limited to considered months. On the other hand, if all filters are chosen, i.e. filters=c(extremeprices,dumpprices,lowsales), then these filters work independently and a summary result is returned. Please note that both variants of extremeprices filter can be chosen at the same time, i.e. plimits and pquantiles, and they work also independently.

References

Van Loon, K., Roels, D. (2018) Integrating big data in Belgian CPI. Meeting of the Group of Experts on Consumer Price Indices, Geneva.

Examples

data_filtering(milk,start="2018-12",end="2019-03",
filters=c("extremeprices"),pquantiles=c(0.01,0.99),interval=TRUE)
data_filtering(milk,start="2018-12",end="2019-03",
filters=c("extremeprices","lowsales"), plimits=c(0.25,2))

Imputing missing and (optionally) zero prices.

Description

This function imputes missing prices and (optionally) zero prices by using carry forward/backward prices.

Usage

data_imputing(data, start, end, zero_prices = TRUE, outlets = TRUE)

Arguments

Value

This function imputes missing prices (unit values) and (optionally) zero prices by using carry forward/backward prices. The imputation can be done for each outlet separately or for aggragated data (see the outlets parameter). If a missing product has a previous price then that previous price is carried forward until the next real observation. If there is no previous price then the next real observation is found and carried backward. The quantities for imputed prices are set to zeros. The function returns a data frame (monthly aggregated) which is ready for price index calculations.

Examples

# Creating a small data set with zero prices:
time.<-c("2018-12-01","2019-01-01")
time<-as.Date(c(time., time.))
p1<-c(0,23)
p2<-c(14,0)
q1<-c(15,25)
q2<-c(44,79)
quantities<-c(q1,q2)
prices<-c(p1,p2)
prodID<-c(1,1,2,2)
my_data<-data.frame(time, prices, quantities, prodID)
# Price imputing:
data_imputing(my_data, start="2018-12", end="2019-01",
zero_prices=TRUE, outlets=FALSE)

# Preparing a data set with zero and missing prices:
dataMATCH$prodID<-dataMATCH$codeIN 
data<-dplyr::select(dataMATCH, time, prices, quantities, prodID, retID)
set1<-data[1:5,]
set1$prices<-0
set2<-data[6:30,]
df<-rbind(set1, set2)
# Price imputing:
data_imputing(df, start="2018-12", end="2019-03",
zero_prices=TRUE, outlets=TRUE)

Matching products

Description

This function returns a data set defined in the first parameter (data) with an additional column (prodID). Two products are treated as being matched if they have the same prodID value.

Usage

data_matching(
  data,
  start,
  end,
  interval = FALSE,
  variables = c(),
  codeIN = TRUE,
  codeOUT = TRUE,
  description = TRUE,
  onlydescription = FALSE,
  precision = 0.95
)

Arguments

Value

This function returns a data set defined in the first parameter (data) with an additional column (prodID). Two products are treated as being matched if they have the same prodID value. The procedure of generating the above-mentioned additional column depends on the set of chosen columns for matching. In most extreme case, when the onlydescription parameter value is TRUE, two products are also matched if they have identical descriptions. Other cases are as follows: Case 1: Parameters codeIN, codeOUT and description are set to TRUE. Products with two identical codes or one of the codes identical and an identical description are automatically matched. Products are also matched if they have identical one of codes and the Jaro-Winkler similarity of their descriptions is bigger than the precision value.Case 2: Only one of the parameters: codeIN or codeOUT are set to TRUE and also the description parameter is set to TRUE. Products with an identical chosen code and an identical description are automatically matched. In the second stage, products are also matched if they have an identical chosen code and the Jaro-Winkler similarity of their descriptions is bigger than the precision value. Case 3: Parameters codeIN and codeOUT are set to TRUE and the parameter description is set to FALSE. In this case, products are matched if they have both codes identical. Case 4: Only the parameter description is set to TRUE. This case requires the onlydescription parameter to be TRUE and then the matching process is based only on product labels (two products are matched if they have identical descriptions). Case 5: Only one of the parameters: codeIN or codeOUT are set to TRUE and the description parameter is set to FALSE. In this case, the only reasonable option is to return the prodID column which is identical with the chosen code column. Please note that if the set of column names defined in the variables parameter is not empty, then the values of these additional columns must be identical while product matching.

Examples

data_matching(dataMATCH, start="2018-12",end="2019-02",onlydescription=TRUE,interval=TRUE)
data_matching(dataMATCH, start="2018-12",end="2019-02",precision=0.98, interval=TRUE)

Normalization of grammage units and recalculation of prices and quantities with respect to these units

Description

The function normalizes grammage units of products and recalculates product prices and quantities with respect to these normalized grammage units.

Usage

data_norm(
  data = data.frame(),
  rules = list(c("ml", "l", 1000), c("g", "kg", 1000)),
  all = TRUE
)

Arguments

Value

The function returns the user's data frame with two transformed columns: grammage and unit, and two rescaled columns: prices and quantities. The above-mentioned transformation and rescaling take into consideration the user rules. Recalculated prices and quantities concern grammage units defined as the second parameter in the given rule.

Examples

# Preparing a data set
data<-data_unit(dataU, units=c("g|ml|kg|l"), multiplication="x")
# Normalization of grammage units
data_norm(data, rules=list(c("ml","l",1000), c("g","kg",1000)))

Preparing a data set for further data processing or price index calculations

Description

This function returns a prepared data frame based on the user's data set. The resulting data frame is ready for further data processing (such as data selecting, matching or filtering) and it is also ready for price index calculations (if only it contains required columns).

Usage

data_preparing(
  data,
  time = NULL,
  prices = NULL,
  quantities = NULL,
  prodID = NULL,
  retID = NULL,
  description = NULL,
  codeIN = NULL,
  codeOUT = NULL,
  grammage = NULL,
  unit = NULL,
  additional = c(),
  zero_prices = FALSE,
  zero_quantities = TRUE
)

Arguments

Value

The resulting data frame is free from: missing values, negative prices (if zero_prices is set to TRUE), zero or negative prices (if zero_prices is set to FALSE), negative quantities (if zero_quantities is set to TRUE) and zero and negative quantities (if zero_prices is set to FALSE). As a result, column time is set to be Date type (in format: 'Year-Month-01'), columns prices and quantities are set to be numeric. If the column description is selected, then it is set to be character type. If columns: prodID, retID, codeIN or codeOUT are selected, then they are set to be factor type.

Examples

data_preparing(milk, time="time",prices="prices",quantities="quantities")
data_preparing(dataCOICOP, time="time",
prices="prices",quantities="quantities",additional="coicop6")

Reducing products

Description

The function returns a reduced data set, i.e. a data set containing sufficiently numerous matched products in the indicated groups. The input data set (data frame) must contain matched products over time, i.e. it must contain the prodID column (as numeric, factor or character), or product descriptions, i.e. it must contain the description column (as character).

Usage

data_reducing(
  data,
  start,
  end,
  type = "prodID",
  minN = 2,
  outlets = FALSE,
  by = c(),
  interval = FALSE
)

Arguments

Value

The function returns a reduced data set, i.e. a data set containing sufficiently numerous matched products in the indicated groups. For each product group created and for selected periods, the procedure checks that the count of identical prodIDs (or identical product descriptions, which does not necessarily mean the same thing) is at least equal to minN. If it is not, such products are eliminated from the data set. The function performs the check either only for the base and current period (in which case the interval parameter is FALSE) or also for all intermediate months (in which case the interval parameter is TRUE). If the user wants to perform this check for each outlet separately, then the outlets parameter should be set to TRUE.

Examples

data_reducing(sugar, start="2018-12", end="2019-12",by="description", minN=5)

Selecting products from the user's data set for further price index calculations

Description

The function returns a subset of the user's data set obtained by selection based on keywords and phrases.

Usage

data_selecting(
  data,
  include = c(),
  must = c(),
  exclude = c(),
  sensitivity = FALSE,
  coicop = NULL
)

Arguments

Value

The function returns a subset of the user's data set obtained by selection based on keywords and phrases defined by parameters: include, must and exclude (an additional column coicop is optional). Providing values of these parameters, please remember that the procedure distinguishes between uppercase and lowercase letters only when sensitivity is set to TRUE.

Examples

data_selecting(milk, include=c("milk"), must=c("UHT"))
data_selecting(milk, must=c("milk"), exclude=c("past"))

Providing information about the grammage and unit of products

Description

The function returns the grammage and unit of products as two additional columns.

Usage

data_unit(data = data.frame(), units = c("g|ml|kg|l"), multiplication = "x")

Arguments

Value

The function returns the user's data frame with two additional columns: grammage and unit. The values of these columns are extracted from product descriptions on the basis of provided units. Please note, that the function takes into consideration a sign of the multiplication, e.g. if the product description contains: '2x50 g', we obtain: grammage: 100 and unit: g for that product (for multiplication set to 'x').

Examples

data_unit(dataU, units=c("g|ml|kg|l"), multiplication="x")

Calculating the bilateral Davies price index

Description

This function returns a value (or vector of values) of the bilateral Davies price index.

Usage

davies(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the bilateral Davies price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Davies, G. R. (1924). The Problem of a Standard Index Number Formula. Journal of the American Statistical Association, 19 (146), 180-188.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Von der Lippe, P. (2007). Index Theory and Price Statistics. Peter Lang: Berlin, Germany.

Examples

davies(sugar, start="2018-12", end="2019-12")
davies(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the unweighted Dikhanov price index

Description

This function returns a value (or vector of values) of the unweighted bilateral Dikhanov price index.

Usage

dikhanov(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the unweighted bilateral Dikhanov price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Dikhanov, Y., (2024). A New Elementary Index Number. Paper presented at the 18th Meeting of the Ottawa Group on Price Indices, Ottawa, Canada.

Examples

dikhanov(sugar, start="2018-12", end="2019-12")
dikhanov(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the relative price and/or quantity dissimilarity measure between periods

Description

This function returns a value of the relative price and/or quantity dissimilarity measure.

Usage

dissimilarity(data, period1, period2, type = "p")

Arguments

Value

This function returns a value of the relative price (dSP) and/or quantity (dSQ) dissimilarity measure. In a special case, when the type parameter is set to pq, the function provides the value of dSPQ measure (the relative price and quantity dissimilarity measure calculated as min(dSP,dSQ).

References

Diewert, E. (2020). The Chain Drift Problem and Multilateral Indexes. Chapter 6 in: Consumer Price Index Theory (draft)

Examples

dissimilarity(milk, period1="2018-12",period2="2019-12",type="q")
dissimilarity(milk, period1="2018-12",period2="2019-12",type="pq")

Presenting the relative price and/or quantity dissimilarity measure over time

Description

This function presents values of the relative price and/or quantity dissimilarity measure over time.

Usage

dissimilarity_fig(
  data,
  start,
  end,
  type = "p",
  benchmark = "end",
  figure = TRUE,
  date_breaks = "1 month"
)

Arguments

Value

This function presents values of the relative price and/or quantity dissimilarity measure over time. The user can choose a benchmark period (defined by benchmark) and the type of dissimilarity measure is to be calculated (defined by type). The obtained results of dissimilarities over time can be presented in a dataframe form or via a figure (the default value of figure is TRUE, which results in a figure).

References

Diewert, E. (2020). The Chain Drift Problem and Multilateral Indexes. Chapter 6 in: Consumer Price Index Theory (draft)

Examples

dissimilarity_fig(milk, start="2018-12",end="2019-12",type="q",figure=FALSE)
dissimilarity_fig(milk, start="2018-12",end="2019-12",type="pq",benchmark="start")

Calculating the bilateral Drobisch price index

Description

This function returns a value (or vector of values) of the bilateral Drobisch price index.

Usage

drobisch(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the bilateral Drobisch price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Drobisch, M. W. (1871). Ueber einige Einwurfe gegen die in diesen Jahrbuchern veroffentlichte neue Methode, die Veranderungen der Waarenpreise und des Geldwerths zu berechten.Jahrbucher fur Nationalokonomie und Statistik, Vol. 16, s. 416-427.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Von der Lippe, P. (2007). Index Theory and Price Statistics. Peter Lang: Berlin, Germany.

Examples

drobisch(sugar, start="2018-12", end="2019-12")
drobisch(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the unweighted Dutot price index

Description

This function returns a value (or vector of values) of the unweighted bilateral Dutot price index.

Usage

dutot(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the unweighted bilateral Dutot price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Dutot, C. F., (1738). Reflexions Politiques sur les Finances et le Commerce. The Hague: Les Freres Vaillant et Nicolas Prevost, Vol. 1.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

dutot(sugar, start="2018-12", end="2019-12")
dutot(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the elasticity of substitution

Description

This function returns a value of the elasticity of substitution

Usage

elasticity(
  data,
  start,
  end,
  method = "lm",
  left = -10,
  right = 10,
  precision = 1e-06
)

Arguments

Value

This function returns a value of the elasticity of substitution. If the method parameter is set to lm, the procedure of estimation solves the equation: LM(sigma)-CW(sigma)=0 numerically, where LM denotes the Lloyd-Moulton price index, the CW denotes a current weight counterpart of the Lloyd-Moulton price index, and sigma is the elasticity of substitution parameter, which is estimated. If the method parameter is set to f, the Fisher price index formula is used instead of the CW price index. If the method parameter is set to t, the Tornqvist price index formula is used instead of the CW price index. If the method parameter is set to w, the Walsh price index formula is used instead of the CW price index. If the method parameter is set to sv, the Sato-Vartia price index formula is used instead of the CW price index.The procedure continues until the absolute value of this difference is greater than the value of the 'precision' parameter.

References

de Haan, J., Balk, B.M., Hansen, C.B. (2010). Retrospective Approximations of Superlative Price Indexes for Years Where Expenditure Data Is Unavailable. In: Biggeri, L., Ferrari, G. (eds) Price Indexes in Time and Space. Contributions to Statistics. Physica-Verlag HD.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

elasticity(coffee, start = "2018-12", end = "2019-01")
elasticity(coffee, start = "2018-12", end = "2019-01", method = "f")
elasticity(coffee, start = "2018-12", end = "2019-01", method = "sv")

Presenting elasticities of substitution for time interval

Description

The function provides a data frame or a figure presenting elasticities of substitution calculated for time interval.

Usage

elasticity_fig(
  data,
  start,
  end,
  method = c("lm"),
  fixedbase = TRUE,
  figure = TRUE,
  date_breaks = "1 month",
  names = c(),
  left = -10,
  right = 10,
  precision = 1e-06
)

Arguments

Value

The function provides a data frame or a figure presenting elasticities of substitution calculated for time interval (see the figure parameter). The elasticities of substitution can be calculated for subsequent months or for a fixed base month (see the start parameter) and rest of months from the given time interval (it depends on the fixedbase parameter). The above-mentioned parameters for compared months are calculated by using the elasticity function.

References

de Haan, J., Balk, B.M., Hansen, C.B. (2010). Retrospective Approximations of Superlative Price Indexes for Years Where Expenditure Data Is Unavailable. In: Biggeri, L., Ferrari, G. (eds) Price Indexes in Time and Space. Contributions to Statistics. Physica-Verlag HD.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

elasticity_fig (milk,start="2018-12",end="2019-04",figure=TRUE, 
method=c("lm","f"),names=c("LM","Fisher"))
elasticity_fig (milk,start="2018-12",end="2019-06",figure=FALSE)

Providing expenditures of sold products

Description

The function returns expenditures of sold products with given IDs.

Usage

expenditures(data, period, set = c(), ID = FALSE)

Arguments

Value

The function analyzes the user's data frame and returns expenditures of products with given ID and being sold in the time period indicated by the period parameter. Please note that the function returns the expenditure values for sorted prodIDs and in the absence of a given prodID in the data set, the function returns nothing (it does not return zero). If the ID parameter is set to TRUE then the function returns a data frame with columns: by (IDs of products) and expend (expenditures of products).

Examples

expenditures(milk, period="2019-06")
expenditures(milk, period="2019-12", set=c(400032, 82919), ID=TRUE)

A general function to compute a final price index

Description

This function returns a value (or values) of the selected final price index for the selected type of aggregation of partial results.

Usage

final_index(
  data = data.frame(),
  start = c(),
  end = c(),
  formula = c(),
  window = c(),
  splice = c(),
  base = c(),
  sigma = c(),
  r = c(),
  outlets = FALSE,
  groups = FALSE,
  by = c(),
  aggr = "fisher",
  interval = FALSE
)

Arguments

Value

This general function returns a value or values of the selected final price index for the selected type of aggregation of partial results. If the interval parameter is set to TRUE, then it returns a data frame where its first column indicates dates and the remaining columns show corresponding values of all selected price indices.

Examples

final_index(coffee, start = "2018-12", end = "2019-12", 
         formula = "fisher", groups = TRUE, outlets = FALSE, 
         aggr = "tornqvist", by = "description")
final_index(milk, start = "2018-12", end = "2019-12", 
         formula = "fisher", groups = TRUE, outlets = TRUE, 
         aggr = "laspeyres", by = "description", 
         interval = TRUE)

Calculating the bilateral Fisher price index

Description

This function returns a value (or vector of values) of the bilateral Fisher price index.

Usage

fisher(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the bilateral Fisher price index depending on the interval parameter. If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating,please use the final_index function).

References

Fisher, I. (1922). The Making of Index Numbers. Boston: Houghton Mifflin.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

fisher(sugar, start="2018-12", end="2019-12")
fisher(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the bilateral Geary-Khamis price index

Description

This function returns a value (or vector of values) of the bilateral Geary-Khamis price index.

Usage

geary_khamis(data, start, end, interval = FALSE)

Arguments

Value

The function returns a value (or vector of values) of the bilateral Geary-Khamis price index depending on the interval parameter (please use gk function to calculate the multilateral Geary-Khamis price index). If the interval parameter is set to TRUE, the function returns a vector of price index values without dates. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Geary, R. G. (1958). A Note on Comparisons of Exchange Rates and Purchasing Power between Countries. Journal of the Royal Statistical Society, Series A, 121, 97-99.

Khamis, S. H. (1970). Properties and Conditions for the Existence of a new Type of Index Number. Sankhya Series B32, 81-98.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Von der Lippe, P. (2007). Index Theory and Price Statistics. Peter Lang: Berlin, Germany.

Examples

geary_khamis(sugar, start="2018-12", end="2019-12")
geary_khamis(milk, start="2018-12", end="2020-01", interval=TRUE)

Calculating the multilateral GEKS price index

Description

This function returns a value of the multilateral GEKS price index (to be more precise: the GEKS index based on the Fisher formula).

Usage

geks(data, start, end, wstart = start, window = 13)

Arguments

Value

This function returns a value of the multilateral GEKS price index (to be more precise: the GEKS index based on the Fisher formula) which considers the time window defined by wstart and window parameters. It measures the price dynamics by comparing period end to period start (both start and end must be inside the considered time window). To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Gini, C. (1931). On the Circular Test of Index Numbers. Metron 9:9, 3-24.

Elteto, O., and Koves, P. (1964). On a Problem of Index Number Computation Relating to International Comparisons. Statisztikai Szemle 42, 507-518.

Szulc, B. (1983). Linking Price Index Numbers. In: Price Level Measurement, W. E. Diewert and C. Montmarquette (eds.), 537-566.

Examples

geks(milk, start="2019-01", end="2019-08",window=10)
geks(milk, start="2018-12", end="2019-12")

Extending the multilateral GEKS price index by using the FBEW method.

Description

This function returns a value of the multilateral GEKS price index extended by using the FBEW (Fixed Base Monthly Expanding Window) method.

Usage

geks_fbew(data, start, end)

Arguments

Value

This function returns a value of the multilateral GEKS price index extended by using the FBEW (Fixed Base Monthly Expanding Window) method. The FBEW method uses a time window with a fixed base month every year (December). The window is enlarged every month with one month in order to include information from a new month. The full window length (13 months) is reached in December of each year. The function measures the price dynamics between periods end and start. The month of the start parameter must be December. If the distance between end and start exceeds 13 months, then internal Decembers play a role of chain-linking months. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Gini, C. (1931). On the Circular Test of Index Numbers. Metron 9:9, 3-24.

Elteto, O., and Koves, P. (1964). On a Problem of Index Number Computation Relating to International Comparisons. Statisztikai Szemle 42, 507-518.

Szulc, B. (1983). Linking Price Index Numbers. In: Price Level Measurement, W. E. Diewert and C. Montmarquette (eds.), 537-566.

Chessa, A.G. (2016). A New Methodology for Processing Scanner Data in the Dutch CPI. Eurona 1/2016, 49-69.

Examples

geks_fbew(milk, start="2018-12", end="2019-08")

Extending the multilateral GEKS price index by using the FBMW method.

Description

This function returns a value of the multilateral GEKS price index extended by using the FBMW (Fixed Base Moving Window) method.

Usage

geks_fbmw(data, start, end)

Arguments

Value

This function returns a value of the multilateral GEKS price index extended by using the FBMW (Fixed Base Moving Window) method. It measures the price dynamics between periods end and start and it uses a 13-month time window with a fixed base month taken as year(end)-1. If the distance between end and start exceeds 13 months, then internal Decembers play a role of chain-linking months. The month of the start parameter must be December. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Gini, C. (1931). On the Circular Test of Index Numbers. Metron 9:9, 3-24.

Elteto, O., and Koves, P. (1964). On a Problem of Index Number Computation Relating to International Comparisons. Statisztikai Szemle 42, 507-518.

Szulc, B. (1983). Linking Price Index Numbers. In: Price Level Measurement, W. E. Diewert and C. Montmarquette (eds.), 537-566.

Lamboray, C.(2017). The Geary Khamis index and the Lehr index: how much do they differ? Paper presented at the 15th Ottawa Group meeting, 10-12 May 2017, Elville am Rhein, Germany.

Examples

geks_fbmw(milk, start="2019-12", end="2020-04")

Extending the multilateral GEKS price index by using window splicing methods.

Description

This function returns a value (or values) of the multilateral GEKS price index extended by using window splicing methods. Available splicing methods are: movement splice, window splice, half splice, mean splice and their additional variants: window splice on published indices (WISP), half splice on published indices (HASP) and mean splice on published indices (see References).

Usage

geks_splice(
  data,
  start,
  end,
  window = 13,
  splice = "movement",
  interval = FALSE
)

Arguments

Value

This function returns a value or values (depending on interval parameter) of the multilateral GEKS price index extended by using window splicing methods. Available splicing methods are: movement splice, window splice, half splice, mean splice and their additional variants: window splice on published indices (WISP), half splice on published indices (HASP) and mean splice on published indices (see References). The time window starts in start and should consist of at least two months. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Chessa, A. G. (2019). A Comparison of Index Extension Methods for Multilateral Methods. Paper presented at the 16th Meeting of the Ottawa Group on Price Indices, 8-10 May 2019, Rio de Janeiro, Brazil.

de Haan, J., van der Grient, H.A. (2011). Eliminating chain drift in price indexes based on scanner data. Journal of Econometrics, 161, 36-46.

Krsinich, F. (2014). The FEWS Index: Fixed Effects with a Window Splice? Non-Revisable Quality-Adjusted Price Indices with No Characteristic Information. Paper presented at the UNECE-ILO Meeting of the Group of Experts on Consumer Price Indices, 2-4 May 2016, Geneva, Switzerland.

de Haan, J.(2015). A Framework for Large Scale Use of Scanner Data in the Dutch CPI. Paper presented at the 14th Ottawa Group meeting, Tokyo, Japan.

Diewert, W.E., and Fox, K.J. (2017). Substitution Bias in Multilateral Methods for CPI Construction using Scanner Data. Discussion paper 17-02, Vancouver School of Economics, The University of British Columbia, Vancouver, Canada.

Examples

geks_splice(milk, start="2018-12", end="2020-02",splice="half")

Calculating the multilateral GEKS-AQI price index

Description

This function returns a value of the multilateral GEKS-AQI price index (to be more precise: the GEKS index based on the asynchronous quality adjusted price index formula).

Usage

geksaqi(data, start, end, wstart = start, window = 13)

Arguments

Value

This function returns a value of the multilateral GEKS-AQI price index (to be more precise: the GEKS index based on the asynchronous quality adjusted price index formula) which considers the time window defined by wstart and window parameters. It measures the price dynamics by comparing period end to period start (both start and end must be inside the considered time window). To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Gini, C. (1931). On the Circular Test of Index Numbers. Metron 9:9, 3-24.

Elteto, O., and Koves, P. (1964). On a Problem of Index Number Computation Relating to International Comparisons. Statisztikai Szemle 42, 507-518.

Szulc, B. (1983). Linking Price Index Numbers. In: Price Level Measurement, W. E. Diewert and C. Montmarquette (eds.), 537-566.

Białek, J. (2022). The general class of multilateral indices and its two special cases. Paper presented at the 17th Meeting of the Ottawa Group on Price Indices, Rome, Italy.

Białek, J. (2023). Quality adjusted GEKS-type indices for price comparisons based on scanner data. Statistics in Transition – new series, 24(3), 151-169.

Examples

geksaqi(milk, start="2019-01", end="2019-08",window=10)
geksaqi(milk, start="2018-12", end="2019-12")

Extending the multilateral GEKS-AQI price index by using the FBEW method.

Description

This function returns a value of the multilateral GEKS-AQI price index extended by using the FBEW (Fixed Base Monthly Expanding Window) method.

Usage

geksaqi_fbew(data, start, end)

Arguments

Value

This function returns a value of the multilateral GEKS-AQI price index (the GEKS index based on the asynchronous quality adjusted price index formula) extended by using the FBEW (Fixed Base Monthly Expanding Window) method. The FBEW method uses a time window with a fixed base month every year (December). The window is enlarged every month with one month in order to include information from a new month. The full window length (13 months) is reached in December of each year. The function measures the price dynamics between periods end and start. The month of the start parameter must be December. If the distance between end and start exceeds 13 months, then internal Decembers play a role of chain-linking months. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Gini, C. (1931). On the Circular Test of Index Numbers. Metron 9:9, 3-24.

Elteto, O., and Koves, P. (1964). On a Problem of Index Number Computation Relating to International Comparisons. Statisztikai Szemle 42, 507-518.

Szulc, B. (1983). Linking Price Index Numbers. In: Price Level Measurement, W. E. Diewert and C. Montmarquette (eds.), 537-566.

Chessa, A.G. (2016). A New Methodology for Processing Scanner Data in the Dutch CPI. Eurona 1/2016, 49-69.

Białek, J. (2022). The general class of multilateral indices and its two special cases. Paper presented at the 17th Meeting of the Ottawa Group on Price Indices, Rome, Italy.

Białek, J. (2023). Quality adjusted GEKS-type indices for price comparisons based on scanner data. Statistics in Transition – new series, 24(3), 151-169.

Examples

geksaqi_fbew(milk, start="2018-12", end="2019-08")

Extending the multilateral GEKS-AQI price index by using the FBMW method.

Description

This function returns a value of the multilateral GEKS-AQI price index extended by using the FBMW (Fixed Base Moving Window) method.

Usage

geksaqi_fbmw(data, start, end)

Arguments

Value

This function returns a value of the multilateral GEKS-AQI price index (the GEKS index based on the asynchronous quality adjusted price index formula) extended by using the FBMW (Fixed Base Moving Window) method. It measures the price dynamics between periods end and start and it uses a 13-month time window with a fixed base month taken as year(end)-1. If the distance between end and start exceeds 13 months, then internal Decembers play a role of chain-linking months. The month of the start parameter must be December. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Gini, C. (1931). On the Circular Test of Index Numbers. Metron 9:9, 3-24.

Elteto, O., and Koves, P. (1964). On a Problem of Index Number Computation Relating to International Comparisons. Statisztikai Szemle 42, 507-518.

Szulc, B. (1983). Linking Price Index Numbers. In: Price Level Measurement, W. E. Diewert and C. Montmarquette (eds.), 537-566.

Lamboray, C.(2017). The Geary Khamis index and the Lehr index: how much do they differ? Paper presented at the 15th Ottawa Group meeting, 10-12 May 2017, Elville am Rhein, Germany.

Białek, J. (2022). The general class of multilateral indices and its two special cases. Paper presented at the 17th Meeting of the Ottawa Group on Price Indices, Rome, Italy.

Białek, J. (2023). Quality adjusted GEKS-type indices for price comparisons based on scanner data. Statistics in Transition – new series, 24(3), 151-169.

Examples

geksaqi_fbmw(milk, start="2019-12", end="2020-04")

Extending the multilateral GEKS-AQI price index by using window splicing methods.

Description

This function returns a value (or values) of the multilateral GEKS-AQI price index extended by using window splicing methods. Available splicing methods are: movement splice, window splice, half splice, mean splice and their additional variants: window splice on published indices (WISP), half splice on published indices (HASP) and mean splice on published indices (see References).

Usage

geksaqi_splice(
  data,
  start,
  end,
  window = 13,
  splice = "movement",
  interval = FALSE
)

Arguments

Value

This function returns a value or values (depending on interval parameter) of the multilateral GEKS-AQI price index (the GEKS index based on the asynchronous quality adjusted price index formula) extended by using window splicing methods. Available splicing methods are: movement splice, window splice, half splice, mean splice and their additional variants: window splice on published indices (WISP), half splice on published indices (HASP) and mean splice on published indices (see References). The time window starts in start and should consist of at least two months. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Chessa, A. G. (2019). A Comparison of Index Extension Methods for Multilateral Methods. Paper presented at the 16th Meeting of the Ottawa Group on Price Indices, 8-10 May 2019, Rio de Janeiro, Brazil.

de Haan, J., van der Grient, H.A. (2011). Eliminating chain drift in price indexes based on scanner data. Journal of Econometrics, 161, 36-46.

Krsinich, F. (2014). The FEWS Index: Fixed Effects with a Window Splice? Non-Revisable Quality-Adjusted Price Indices with No Characteristic Information. Paper presented at the UNECE-ILO Meeting of the Group of Experts on Consumer Price Indices, 2-4 May 2016, Geneva, Switzerland.

de Haan, J.(2015). A Framework for Large Scale Use of Scanner Data in the Dutch CPI. Paper presented at the 14th Ottawa Group meeting, Tokyo, Japan.

Diewert, W.E., and Fox, K.J. (2017). Substitution Bias in Multilateral Methods for CPI Construction using Scanner Data. Discussion paper 17-02, Vancouver School of Economics, The University of British Columbia, Vancouver, Canada.

Białek, J. (2022). The general class of multilateral indices and its two special cases. Paper presented at the 17th Meeting of the Ottawa Group on Price Indices, Rome, Italy.

Białek, J. (2023). Quality adjusted GEKS-type indices for price comparisons based on scanner data. Statistics in Transition – new series, 24(3), 151-169.

Examples

geksaqi_splice(milk, start="2018-12", end="2020-02",splice="half")

Calculating the multilateral GEKS-AQU price index

Description

This function returns a value of the multilateral GEKS-AQU price index (to be more precise: the GEKS index based on the asynchronous quality adjusted unit value formula).

Usage

geksaqu(data, start, end, wstart = start, window = 13)

Arguments

Value

This function returns a value of the multilateral GEKS-AQU price index (to be more precise: the GEKS index based on the asynchronous quality adjusted unit value formula) which considers the time window defined by wstart and window parameters. It measures the price dynamics by comparing period end to period start (both start and end must be inside the considered time window). To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Gini, C. (1931). On the Circular Test of Index Numbers. Metron 9:9, 3-24.

Elteto, O., and Koves, P. (1964). On a Problem of Index Number Computation Relating to International Comparisons. Statisztikai Szemle 42, 507-518.

Szulc, B. (1983). Linking Price Index Numbers. In: Price Level Measurement, W. E. Diewert and C. Montmarquette (eds.), 537-566.

Białek, J. (2022). The general class of multilateral indices and its two special cases. Paper presented at the 17th Meeting of the Ottawa Group on Price Indices, Rome, Italy.

Białek, J. (2023). Quality adjusted GEKS-type indices for price comparisons based on scanner data. Statistics in Transition – new series, 24(3), 151-169.

Examples

geksaqu(milk, start="2019-01", end="2019-08",window=10)
geksaqu(milk, start="2018-12", end="2019-12")

Extending the multilateral GEKS-AQU price index by using the FBEW method.

Description

This function returns a value of the multilateral GEKS-AQU price index extended by using the FBEW (Fixed Base Monthly Expanding Window) method.

Usage

geksaqu_fbew(data, start, end)

Arguments

Value

This function returns a value of the multilateral GEKS-AQU price index (the GEKS index based on the asynchronous quality adjusted unit value formula) extended by using the FBEW (Fixed Base Monthly Expanding Window) method. The FBEW method uses a time window with a fixed base month every year (December). The window is enlarged every month with one month in order to include information from a new month. The full window length (13 months) is reached in December of each year. The function measures the price dynamics between periods end and start. The month of the start parameter must be December. If the distance between end and start exceeds 13 months, then internal Decembers play a role of chain-linking months. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating,please use the final_index function).

References

Gini, C. (1931). On the Circular Test of Index Numbers. Metron 9:9, 3-24.

Elteto, O., and Koves, P. (1964). On a Problem of Index Number Computation Relating to International Comparisons. Statisztikai Szemle 42, 507-518.

Szulc, B. (1983). Linking Price Index Numbers. In: Price Level Measurement, W. E. Diewert and C. Montmarquette (eds.), 537-566.

Chessa, A.G. (2016). A New Methodology for Processing Scanner Data in the Dutch CPI. Eurona 1/2016, 49-69.

Białek, J. (2022). The general class of multilateral indices and its two special cases. Paper presented at the 17th Meeting of the Ottawa Group on Price Indices, Rome, Italy.

Białek, J. (2023). Quality adjusted GEKS-type indices for price comparisons based on scanner data. Statistics in Transition – new series, 24(3), 151-169.

Examples

geksaqu_fbew(milk, start="2018-12", end="2019-08")

Extending the multilateral GEKS-AQU price index by using the FBMW method.

Description

This function returns a value of the multilateral GEKS-AQU price index extended by using the FBMW (Fixed Base Moving Window) method.

Usage

geksaqu_fbmw(data, start, end)

Arguments

Value

This function returns a value of the multilateral GEKS-AQU price index (the GEKS index based on the asynchronous quality adjusted unit value formula) extended by using the FBMW (Fixed Base Moving Window) method. It measures the price dynamics between periods end and start and it uses a 13-month time window with a fixed base month taken as year(end)-1. If the distance between end and start exceeds 13 months, then internal Decembers play a role of chain-linking months. The month of the start parameter must be December. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Gini, C. (1931). On the Circular Test of Index Numbers. Metron 9:9, 3-24.

Elteto, O., and Koves, P. (1964). On a Problem of Index Number Computation Relating to International Comparisons. Statisztikai Szemle 42, 507-518.

Szulc, B. (1983). Linking Price Index Numbers. In: Price Level Measurement, W. E. Diewert and C. Montmarquette (eds.), 537-566.

Lamboray, C.(2017). The Geary Khamis index and the Lehr index: how much do they differ? Paper presented at the 15th Ottawa Group meeting, 10-12 May 2017, Elville am Rhein, Germany.

Białek, J. (2022). The general class of multilateral indices and its two special cases. Paper presented at the 17th Meeting of the Ottawa Group on Price Indices, Rome, Italy.

Białek, J. (2023). Quality adjusted GEKS-type indices for price comparisons based on scanner data. Statistics in Transition – new series, 24(3), 151-169.

Examples

geksaqu_fbmw(milk, start="2019-12", end="2020-04")

Extending the multilateral GEKS-AQU price index by using window splicing methods.

Description

This function returns a value (or values) of the multilateral GEKS-AQU price index extended by using window splicing methods. Available splicing methods are: movement splice, window splice, half splice, mean splice and their additional variants: window splice on published indices (WISP), half splice on published indices (HASP) and mean splice on published indices (see References).

Usage

geksaqu_splice(
  data,
  start,
  end,
  window = 13,
  splice = "movement",
  interval = FALSE
)

Arguments

Value

This function returns a value or values (depending on interval parameter) of the multilateral GEKS-AQU price index (the GEKS index based on the asynchronous quality adjusted unit value formula) extended by using window splicing methods. Available splicing methods are: movement splice, window splice, half splice, mean splice and their additional variants: window splice on published indices (WISP), half splice on published indices (HASP) and mean splice on published indices (see References). The time window starts in start and should consist of at least two months. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Chessa, A. G. (2019). A Comparison of Index Extension Methods for Multilateral Methods. Paper presented at the 16th Meeting of the Ottawa Group on Price Indices, 8-10 May 2019, Rio de Janeiro, Brazil.

de Haan, J., van der Grient, H.A. (2011). Eliminating chain drift in price indexes based on scanner data. Journal of Econometrics, 161, 36-46.

Krsinich, F. (2014). The FEWS Index: Fixed Effects with a Window Splice? Non-Revisable Quality-Adjusted Price Indices with No Characteristic Information. Paper presented at the UNECE-ILO Meeting of the Group of Experts on Consumer Price Indices, 2-4 May 2016, Geneva, Switzerland.

de Haan, J.(2015). A Framework for Large Scale Use of Scanner Data in the Dutch CPI. Paper presented at the 14th Ottawa Group meeting, Tokyo, Japan.

Diewert, W.E., and Fox, K.J. (2017). Substitution Bias in Multilateral Methods for CPI Construction using Scanner Data. Discussion paper 17-02, Vancouver School of Economics, The University of British Columbia, Vancouver, Canada.

Białek, J. (2022). The general class of multilateral indices and its two special cases. Paper presented at the 17th Meeting of the Ottawa Group on Price Indices, Rome, Italy.

Białek, J. (2023). Quality adjusted GEKS-type indices for price comparisons based on scanner data. Statistics in Transition – new series, 24(3), 151-169.

Examples

geksaqu_splice(milk, start="2018-12", end="2020-02",splice="half")

Calculating the multilateral GEKS-GAQI price index

Description

This function returns a value of the multilateral GEKS-GAQI price index (to be more precise: the GEKS index based on the geometric asynchronous quality adjusted price index formula).

Usage

geksgaqi(data, start, end, wstart = start, window = 13)

Arguments

Value

This function returns a value of the multilateral GEKS-GAQI price index (to be more precise: the GEKS index based on the geometric asynchronous quality adjusted price index formula) which considers the time window defined by wstart and window parameters. It measures the price dynamics by comparing period end to period start (both start and end must be inside the considered time window). To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Gini, C. (1931). On the Circular Test of Index Numbers. Metron 9:9, 3-24.

Elteto, O., and Koves, P. (1964). On a Problem of Index Number Computation Relating to International Comparisons. Statisztikai Szemle 42, 507-518.

Szulc, B. (1983). Linking Price Index Numbers. In: Price Level Measurement, W. E. Diewert and C. Montmarquette (eds.), 537-566.

Examples

geksgaqi(milk, start="2019-01", end="2019-08",window=10)
geksgaqi(milk, start="2018-12", end="2019-12")

Extending the multilateral GEKS-GAQI price index by using the FBEW method.

Description

This function returns a value of the multilateral GEKS-GAQI price index extended by using the FBEW (Fixed Base Monthly Expanding Window) method.

Usage

geksgaqi_fbew(data, start, end)

Arguments

Value

This function returns a value of the multilateral GEKS-GAQI price index (the GEKS index based on the geometric asynchronous quality adjusted price index formula) extended by using the FBEW (Fixed Base Monthly Expanding Window) method. The FBEW method uses a time window with a fixed base month every year (December). The window is enlarged every month with one month in order to include information from a new month. The full window length (13 months) is reached in December of each year. The function measures the price dynamics between periods end and start. The month of the start parameter must be December. If the distance between end and start exceeds 13 months, then internal Decembers play a role of chain-linking months. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Gini, C. (1931). On the Circular Test of Index Numbers. Metron 9:9, 3-24.

Elteto, O., and Koves, P. (1964). On a Problem of Index Number Computation Relating to International Comparisons. Statisztikai Szemle 42, 507-518.

Szulc, B. (1983). Linking Price Index Numbers. In: Price Level Measurement, W. E. Diewert and C. Montmarquette (eds.), 537-566.

Chessa, A.G. (2016). A New Methodology for Processing Scanner Data in the Dutch CPI. Eurona 1/2016, 49-69.

Examples

geksgaqi_fbew(milk, start="2018-12", end="2019-08")

Extending the multilateral GEKS-GAQI price index by using the FBMW method.

Description

This function returns a value of the multilateral GEKS-GAQI price index extended by using the FBMW (Fixed Base Moving Window) method.

Usage

geksgaqi_fbmw(data, start, end)

Arguments

Value

This function returns a value of the multilateral GEKS-GAQI price index (the GEKS index based on the geometric asynchronous quality adjusted price index formula) extended by using the FBMW (Fixed Base Moving Window) method. It measures the price dynamics between periods end and start and it uses a 13-month time window with a fixed base month taken as year(end)-1. If the distance between end and start exceeds 13 months, then internal Decembers play a role of chain-linking months. The month of the start parameter must be December. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Gini, C. (1931). On the Circular Test of Index Numbers. Metron 9:9, 3-24.

Elteto, O., and Koves, P. (1964). On a Problem of Index Number Computation Relating to International Comparisons. Statisztikai Szemle 42, 507-518.

Szulc, B. (1983). Linking Price Index Numbers. In: Price Level Measurement, W. E. Diewert and C. Montmarquette (eds.), 537-566.

Lamboray, C.(2017). The Geary Khamis index and the Lehr index: how much do they differ? Paper presented at the 15th Ottawa Group meeting, 10-12 May 2017, Elville am Rhein, Germany.

Examples

geksgaqi_fbmw(milk, start="2019-12", end="2020-04")

Extending the multilateral GEKS-GAQI price index by using window splicing methods.

Description

This function returns a value (or values) of the multilateral GEKS-GAQI price index extended by using window splicing methods. Available splicing methods are: movement splice, window splice, half splice, mean splice and their additional variants: window splice on published indices (WISP), half splice on published indices (HASP) and mean splice on published indices (see References).

Usage

geksgaqi_splice(
  data,
  start,
  end,
  window = 13,
  splice = "movement",
  interval = FALSE
)

Arguments

Value

This function returns a value or values (depending on interval parameter) of the multilateral GEKS-GAQI price index (the GEKS index based on the geometric asynchronous quality adjusted price index formula) extended by using window splicing methods. Available splicing methods are: movement splice, window splice, half splice, mean splice and their additional variants: window splice on published indices (WISP), half splice on published indices (HASP) and mean splice on published indices (see References). The time window starts in start and should consist of at least two months. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Chessa, A. G. (2019). A Comparison of Index Extension Methods for Multilateral Methods. Paper presented at the 16th Meeting of the Ottawa Group on Price Indices, 8-10 May 2019, Rio de Janeiro, Brazil.

de Haan, J., van der Grient, H.A. (2011). Eliminating chain drift in price indexes based on scanner data. Journal of Econometrics, 161, 36-46.

Krsinich, F. (2014). The FEWS Index: Fixed Effects with a Window Splice? Non-Revisable Quality-Adjusted Price Indices with No Characteristic Information. Paper presented at the UNECE-ILO Meeting of the Group of Experts on Consumer Price Indices, 2-4 May 2016, Geneva, Switzerland.

de Haan, J.(2015). A Framework for Large Scale Use of Scanner Data in the Dutch CPI. Paper presented at the 14th Ottawa Group meeting, Tokyo, Japan.

Diewert, W.E., and Fox, K.J. (2017). Substitution Bias in Multilateral Methods for CPI Construction using Scanner Data. Discussion paper 17-02, Vancouver School of Economics, The University of British Columbia, Vancouver, Canada.

Białek, J. (2022). The general class of multilateral indices and its two special cases. Paper presented at the 17th Meeting of the Ottawa Group on Price Indices, Rome, Italy.

Examples

geksgaqi_splice(milk, start="2018-12", end="2020-01",window=10)

Calculating the multilateral GEKS-GL price index

Description

This function returns a value of the multilateral GEKS-GL price index (to be more precise: the GEKS index based on the geometric Laspeyres formula).

Usage

geksgl(data, start, end, wstart = start, window = 13)

Arguments

Value

This function returns a value of the multilateral GEKS-GL price index (to be more precise: the GEKS index based on the geometric Laspeyres formula) which considers the time window defined by wstart and window parameters. It measures the price dynamics by comparing period end to period start (both start and end must be inside the considered time window). To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Gini, C. (1931). On the Circular Test of Index Numbers. Metron 9:9, 3-24.

Elteto, O., and Koves, P. (1964). On a Problem of Index Number Computation Relating to International Comparisons. Statisztikai Szemle 42, 507-518.

Szulc, B. (1983). Linking Price Index Numbers. In: Price Level Measurement, W. E. Diewert and C. Montmarquette (eds.), 537-566.

Białek, J. (2022). The general class of multilateral indices and its two special cases. Paper presented at the 17th Meeting of the Ottawa Group on Price Indices, Rome, Italy.

Białek, J. (2022). Improving quality of the scanner CPI: proposition of new multilateral methods, Quality & Quantity, https://doi.org/10.1007/s11135-022-01506-6.

Examples

geksgl(milk, start="2019-01", end="2019-08",window=10)
geksgl(milk, start="2018-12", end="2019-12")

Extending the multilateral GEKS-GL price index by using the FBEW method.

Description

This function returns a value of the multilateral GEKS-GL price index extended by using the FBEW (Fixed Base Monthly Expanding Window) method.

Usage

geksgl_fbew(data, start, end)

Arguments

Value

This function returns a value of the multilateral GEKS-GL price index (the GEKS index based on the geometric Laspeyres formula) extended by using the FBEW (Fixed Base Monthly Expanding Window) method. The FBEW method uses a time window with a fixed base month every year (December). The window is enlarged every month with one month in order to include information from a new month. The full window length (13 months) is reached in December of each year. The function measures the price dynamics between periods end and start. The month of the start parameter must be December. If the distance between end and start exceeds 13 months, then internal Decembers play a role of chain-linking months. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Gini, C. (1931). On the Circular Test of Index Numbers. Metron 9:9, 3-24.

Elteto, O., and Koves, P. (1964). On a Problem of Index Number Computation Relating to International Comparisons. Statisztikai Szemle 42, 507-518.

Szulc, B. (1983). Linking Price Index Numbers. In: Price Level Measurement, W. E. Diewert and C. Montmarquette (eds.), 537-566.

Chessa, A.G. (2016). A New Methodology for Processing Scanner Data in the Dutch CPI. Eurona 1/2016, 49-69.

Białek, J. (2022). The general class of multilateral indices and its two special cases. Paper presented at the 17th Meeting of the Ottawa Group on Price Indices, Rome, Italy.

Białek, J. (2022). Improving quality of the scanner CPI: proposition of new multilateral methods, Quality & Quantity, https://doi.org/10.1007/s11135-022-01506-6.

Examples

geksgl_fbew(milk, start="2018-12", end="2019-08")

Extending the multilateral GEKS-GL price index by using the FBMW method.

Description

This function returns a value of the multilateral GEKS-GL price index extended by using the FBMW (Fixed Base Moving Window) method.

Usage

geksgl_fbmw(data, start, end)

Arguments

Value

This function returns a value of the multilateral GEKS-GL price index (the GEKS index based on the geometric Laspeyres formula) extended by using the FBMW (Fixed Base Moving Window) method. It measures the price dynamics between periods end and start and it uses a 13-month time window with a fixed base month taken as year(end)-1. If the distance between end and start exceeds 13 months, then internal Decembers play a role of chain-linking months. The month of the start parameter must be December. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Gini, C. (1931). On the Circular Test of Index Numbers. Metron 9:9, 3-24.

Elteto, O., and Koves, P. (1964). On a Problem of Index Number Computation Relating to International Comparisons. Statisztikai Szemle 42, 507-518.

Szulc, B. (1983). Linking Price Index Numbers. In: Price Level Measurement, W. E. Diewert and C. Montmarquette (eds.), 537-566.

Lamboray, C.(2017). The Geary Khamis index and the Lehr index: how much do they differ? Paper presented at the 15th Ottawa Group meeting, 10-12 May 2017, Elville am Rhein, Germany.

Białek, J. (2022). The general class of multilateral indices and its two special cases. Paper presented at the 17th Meeting of the Ottawa Group on Price Indices, Rome, Italy.

Białek, J. (2022). Improving quality of the scanner CPI: proposition of new multilateral methods, Quality & Quantity, https://doi.org/10.1007/s11135-022-01506-6.

Examples

geksgl_fbmw(milk, start="2019-12", end="2020-04")

Extending the multilateral GEKS-GL price index by using window splicing methods.

Description

This function returns a value (or values) of the multilateral GEKS-GL price index extended by using window splicing methods. Available splicing methods are: movement splice, window splice, half splice, mean splice and their additional variants: window splice on published indices (WISP), half splice on published indices (HASP) and mean splice on published indices (see References).

Usage

geksgl_splice(
  data,
  start,
  end,
  window = 13,
  splice = "movement",
  interval = FALSE
)

Arguments

Value

This function returns a value or values (depending on interval parameter) of the multilateral GEKS-GL price index (the GEKS index based on the geometric Laspeyres formula) extended by using window splicing methods. Available splicing methods are: movement splice, window splice, half splice, mean splice and their additional variants: window splice on published indices (WISP), half splice on published indices (HASP) and mean splice on published indices (see References). The time window starts in start and should consist of at least two months. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Chessa, A. G. (2019). A Comparison of Index Extension Methods for Multilateral Methods. Paper presented at the 16th Meeting of the Ottawa Group on Price Indices, 8-10 May 2019, Rio de Janeiro, Brazil.

de Haan, J., van der Grient, H.A. (2011). Eliminating chain drift in price indexes based on scanner data. Journal of Econometrics, 161, 36-46.

Krsinich, F. (2014). The FEWS Index: Fixed Effects with a Window Splice? Non-Revisable Quality-Adjusted Price Indices with No Characteristic Information. Paper presented at the UNECE-ILO Meeting of the Group of Experts on Consumer Price Indices, 2-4 May 2016, Geneva, Switzerland.

de Haan, J.(2015). A Framework for Large Scale Use of Scanner Data in the Dutch CPI. Paper presented at the 14th Ottawa Group meeting, Tokyo, Japan.

Diewert, W.E., and Fox, K.J. (2017). Substitution Bias in Multilateral Methods for CPI Construction using Scanner Data. Discussion paper 17-02, Vancouver School of Economics, The University of British Columbia, Vancouver, Canada.

Białek, J. (2022). The general class of multilateral indices and its two special cases. Paper presented at the 17th Meeting of the Ottawa Group on Price Indices, Rome, Italy.

Białek, J. (2022). Improving quality of the scanner CPI: proposition of new multilateral methods, Quality & Quantity, https://doi.org/10.1007/s11135-022-01506-6.

Examples

geksgl_splice(milk, start="2018-12", end="2020-02",splice="half")

Calculating the multilateral GEKS-IQM price index

Description

This function returns a value of the multilateral GEKS-IQM price index (to be more precise: the GEKS index based on the implicit quadratic mean of order r price index IQMp).

Usage

geksiqm(data, start, end, r = 2, wstart = start, window = 13)

Arguments

Value

This function returns a value of the multilateral GEKS-IQM price index (to be more precise: the GEKS index based on the the implicit quadratic mean of order r price index IQMp) which considers the time window defined by wstart and window parameters. It measures the price dynamics by comparing period end to period start (both start and end must be inside the considered time window). To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Gini, C. (1931). On the Circular Test of Index Numbers. Metron 9:9, 3-24.

Elteto, O., and Koves, P. (1964). On a Problem of Index Number Computation Relating to International Comparisons. Statisztikai Szemle 42, 507-518.

Szulc, B. (1983). Linking Price Index Numbers. In: Price Level Measurement, W. E. Diewert and C. Montmarquette (eds.), 537-566.

(2004). Consumer Price Index Manual. Theory and practice. ILO/IMF/OECD/UNECE/Eurostat/The World Bank, International Labour Office (ILO), Geneva.

Examples

geksiqm(milk, start="2019-01", end="2019-08",window=10)
geksiqm(milk, start="2018-12", end="2019-12", r=1.6)

Extending the multilateral GEKS-IQM price index by using the FBEW method.

Description

This function returns a value of the multilateral GEKS-IQM price index extended by using the FBEW (Fixed Base Monthly Expanding Window) method.

Usage

geksiqm_fbew(data, start, end, r)

Arguments

Value

This function returns a value of the multilateral GEKS-IQM price index extended by using the FBEW (Fixed Base Monthly Expanding Window) method. The FBEW method uses a time window with a fixed base month every year (December). The window is enlarged every month with one month in order to include information from a new month. The full window length (13 months) is reached in December of each year. The function measures the price dynamics between periods end and start. The month of the start parameter must be December. If the distance between end and start exceeds 13 months, then internal Decembers play a role of chain-linking months. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Gini, C. (1931). On the Circular Test of Index Numbers. Metron 9:9, 3-24.

Elteto, O., and Koves, P. (1964). On a Problem of Index Number Computation Relating to International Comparisons. Statisztikai Szemle 42, 507-518.

Szulc, B. (1983). Linking Price Index Numbers. In: Price Level Measurement, W. E. Diewert and C. Montmarquette (eds.), 537-566.

Chessa, A.G. (2016). A New Methodology for Processing Scanner Data in the Dutch CPI. Eurona 1/2016, 49-69.

Examples

geksiqm_fbew(milk, start="2018-12", end="2019-08", r=1.2)

Extending the multilateral GEKS-IQM price index by using the FBMW method.

Description

This function returns a value of the multilateral GEKS-IQM price index extended by using the FBMW (Fixed Base Moving Window) method.

Usage

geksiqm_fbmw(data, start, end, r)

Arguments

Value

This function returns a value of the multilateral GEKS-IQM price index extended by using the FBMW (Fixed Base Moving Window) method. It measures the price dynamics between periods end and start and it uses a 13-month time window with a fixed base month taken as year(end)-1. If the distance between end and start exceeds 13 months, then internal Decembers play a role of chain-linking months. The month of the start parameter must be December. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Gini, C. (1931). On the Circular Test of Index Numbers. Metron 9:9, 3-24.

Elteto, O., and Koves, P. (1964). On a Problem of Index Number Computation Relating to International Comparisons. Statisztikai Szemle 42, 507-518.

Szulc, B. (1983). Linking Price Index Numbers. In: Price Level Measurement, W. E. Diewert and C. Montmarquette (eds.), 537-566.

Lamboray, C.(2017). The Geary Khamis index and the Lehr index: how much do they differ? Paper presented at the 15th Ottawa Group meeting, 10-12 May 2017, Elville am Rhein, Germany.

Examples

geksiqm_fbmw(milk, start="2019-12", end="2020-04", r=1.6)

Extending the multilateral GEKS-IQM price index by using window splicing methods.

Description

This function returns a value (or values) of the multilateral GEKS-IQM price index extended by using window splicing methods. Available splicing methods are: movement splice, window splice, half splice, mean splice and their additional variants: window splice on published indices (WISP), half splice on published indices (HASP) and mean splice on published indices (see References).

Usage

geksiqm_splice(
  data,
  start,
  end,
  r = 2,
  window = 13,
  splice = "movement",
  interval = FALSE
)

Arguments

Value

This function returns a value or values (depending on interval parameter) of the multilateral GEKS-IQM price index extended by using window splicing methods. Available splicing methods are: movement splice, window splice, half splice, mean splice and their additional variants: window splice on published indices (WISP), half splice on published indices (HASP) and mean splice on published indices (see References). The time window starts in start and should consist of at least two months. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Chessa, A. G. (2019). A Comparison of Index Extension Methods for Multilateral Methods. Paper presented at the 16th Meeting of the Ottawa Group on Price Indices, 8-10 May 2019, Rio de Janeiro, Brazil.

de Haan, J., van der Grient, H.A. (2011). Eliminating chain drift in price indexes based on scanner data. Journal of Econometrics, 161, 36-46.

Krsinich, F. (2014). The FEWS Index: Fixed Effects with a Window Splice? Non-Revisable Quality-Adjusted Price Indices with No Characteristic Information. Paper presented at the UNECE-ILO Meeting of the Group of Experts on Consumer Price Indices, 2-4 May 2016, Geneva, Switzerland.

de Haan, J.(2015). A Framework for Large Scale Use of Scanner Data in the Dutch CPI. Paper presented at the 14th Ottawa Group meeting, Tokyo, Japan.

Diewert, W.E., and Fox, K.J. (2017). Substitution Bias in Multilateral Methods for CPI Construction using Scanner Data. Discussion paper 17-02, Vancouver School of Economics, The University of British Columbia, Vancouver, Canada.

Examples

geksiqm_splice(milk, start="2018-12", end="2020-02", r=0.8, splice="half")

Calculating the multilateral GEKS price index based on the Jevons formula (typical notation: GEKS-J)

Description

This function returns a value of the multilateral GEKS-J price index (to be more precise: the GEKS index based on the Jevons formula).

Usage

geksj(data, start, end, wstart = start, window = 13)

Arguments

Value

This function returns a value of the multilateral GEKS-J price index (to be more precise: the GEKS index based on the Jevons formula) which considers the time window defined by wstart and window parameters. It measures the price dynamics by comparing period end to period start (both start and end must be inside the considered time window). To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Gini, C. (1931). On the Circular Test of Index Numbers. Metron 9:9, 3-24.

Elteto, O., and Koves, P. (1964). On a Problem of Index Number Computation Relating to International Comparisons. Statisztikai Szemle 42, 507-518.

Szulc, B. (1983). Linking Price Index Numbers. In: Price Level Measurement, W. E. Diewert and C. Montmarquette (eds.), 537-566.

Examples

geksj(milk, start="2019-01", end="2019-08",window=10)
geksj(milk, start="2018-12", end="2019-12")

Extending the multilateral GEKS-J price index by using the FBEW method.

Description

This function returns a value of the multilateral GEKS-J price index (i.e. the GEKS price index based on the Jevons formula) extended by using the FBEW (Fixed Base Monthly Expanding Window) method.

Usage

geksj_fbew(data, start, end)

Arguments

Value

This function returns a value of the multilateral GEKS-J price index (i.e. the GEKS price index based on the Jevons formula) extended by using the FBEW (Fixed Base Monthly Expanding Window) method. The FBEW method uses a time window with a fixed base month every year (December). The window is enlarged every month with one month in order to include information from a new month. The full window length (13 months) is reached in December of each year. The function measures the price dynamics between periods end and start. The month of the start parameter must be December. If the distance between end and start exceeds 13 months, then internal Decembers play a role of chain-linking months. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Gini, C. (1931). On the Circular Test of Index Numbers. Metron 9:9, 3-24.

Elteto, O., and Koves, P. (1964). On a Problem of Index Number Computation Relating to International Comparisons. Statisztikai Szemle 42, 507-518.

Szulc, B. (1983). Linking Price Index Numbers. In: Price Level Measurement, W. E. Diewert and C. Montmarquette (eds.), 537-566.

Chessa, A.G. (2016). A New Methodology for Processing Scanner Data in the Dutch CPI. Eurona 1/2016, 49-69.

Examples

geksj_fbew(milk, start="2018-12", end="2019-08")

Extending the multilateral GEKS-J price index by using the FBMW method.

Description

This function returns a value of the multilateral GEKS-J price index (i.e. the GEKS price index based on the Jevons formula) extended by using the FBMW (Fixed Base Moving Window) method.

Usage

geksj_fbmw(data, start, end)

Arguments

Value

This function returns a value of the multilateral GEKS-J price index (i.e. the GEKS price index based on the Jevons formula) extended by using the FBMW (Fixed Base Moving Window) method. It measures the price dynamics between periods end and start and it uses a 13-month time window with a fixed base month taken as year(end)-1. If the distance between end and start exceeds 13 months, then internal Decembers play a role of chain-linking months. The month of the start parameter must be December. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Gini, C. (1931). On the Circular Test of Index Numbers. Metron 9:9, 3-24.

Elteto, O., and Koves, P. (1964). On a Problem of Index Number Computation Relating to International Comparisons. Statisztikai Szemle 42, 507-518.

Szulc, B. (1983). Linking Price Index Numbers. In: Price Level Measurement, W. E. Diewert and C. Montmarquette (eds.), 537-566.

Lamboray, C.(2017). The Geary Khamis index and the Lehr index: how much do they differ? Paper presented at the 15th Ottawa Group meeting, 10-12 May 2017, Elville am Rhein, Germany.

Examples

geksj_fbmw(milk, start="2019-12", end="2020-04")

Extending the multilateral GEKS-J price index by using window splicing methods.

Description

This function returns a value (or values) of the multilateral GEKS-J price index (GEKS based on the Jevons formula) extended by using window splicing methods. Available splicing methods are: movement splice, window splice, half splice, mean splice and their additional variants: window splice on published indices (WISP), half splice on published indices (HASP) and mean splice on published indices (see References).

Usage

geksj_splice(
  data,
  start,
  end,
  window = 13,
  splice = "movement",
  interval = FALSE
)

Arguments

Value

This function returns a value or values (depending on interval parameter) of the multilateral GEKS-J price index extended by using window splicing methods. Available splicing methods are: movement splice, window splice, half splice, mean splice and their additional variants: window splice on published indices (WISP), half splice on published indices (HASP) and mean splice on published indices (see References). The time window starts in start and should consist of at least two months. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Chessa, A. G. (2019). A Comparison of Index Extension Methods for Multilateral Methods. Paper presented at the 16th Meeting of the Ottawa Group on Price Indices, 8-10 May 2019, Rio de Janeiro, Brazil.

de Haan, J., van der Grient, H.A. (2011). Eliminating chain drift in price indexes based on scanner data. Journal of Econometrics, 161, 36-46.

Krsinich, F. (2014). The FEWS Index: Fixed Effects with a Window Splice? Non-Revisable Quality-Adjusted Price Indices with No Characteristic Information. Paper presented at the UNECE-ILO Meeting of the Group of Experts on Consumer Price Indices, 2-4 May 2016, Geneva, Switzerland.

de Haan, J.(2015). A Framework for Large Scale Use of Scanner Data in the Dutch CPI. Paper presented at the 14th Ottawa Group meeting, Tokyo, Japan.

Diewert, W.E., and Fox, K.J. (2017). Substitution Bias in Multilateral Methods for CPI Construction using Scanner Data. Discussion paper 17-02, Vancouver School of Economics, The University of British Columbia, Vancouver, Canada.

Examples

geksj_splice(milk, start="2018-12", end="2020-02",splice="half")

Calculating the multilateral GEKS-L price index

Description

This function returns a value of the multilateral GEKS-L price index (to be more precise: the GEKS index based on the Laspeyres formula).

Usage

geksl(data, start, end, wstart = start, window = 13)

Arguments

Value

This function returns a value of the multilateral GEKS-L price index (to be more precise: the GEKS index based on the Laspeyres formula) which considers the time window defined by wstart and window parameters. It measures the price dynamics by comparing period end to period start (both start and end must be inside the considered time window). To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Gini, C. (1931). On the Circular Test of Index Numbers. Metron 9:9, 3-24.

Elteto, O., and Koves, P. (1964). On a Problem of Index Number Computation Relating to International Comparisons. Statisztikai Szemle 42, 507-518.

Szulc, B. (1983). Linking Price Index Numbers. In: Price Level Measurement, W. E. Diewert and C. Montmarquette (eds.), 537-566.

Białek, J. (2022). The general class of multilateral indices and its two special cases. Paper presented at the 17th Meeting of the Ottawa Group on Price Indices, Rome, Italy.

Białek, J. (2022). Improving quality of the scanner CPI: proposition of new multilateral methods, Quality & Quantity, https://doi.org/10.1007/s11135-022-01506-6.

Examples

geksl(milk, start="2019-01", end="2019-08",window=10)
geksl(milk, start="2018-12", end="2019-12")

Extending the multilateral GEKS-L price index by using the FBEW method.

Description

This function returns a value of the multilateral GEKS-L price index extended by using the FBEW (Fixed Base Monthly Expanding Window) method.

Usage

geksl_fbew(data, start, end)

Arguments

Value

This function returns a value of the multilateral GEKS-L price index (the GEKS index based on the Laspeyres formula) extended by using the FBEW (Fixed Base Monthly Expanding Window) method. The FBEW method uses a time window with a fixed base month every year (December). The window is enlarged every month with one month in order to include information from a new month. The full window length (13 months) is reached in December of each year. The function measures the price dynamics between periods end and start. The month of the start parameter must be December. If the distance between end and start exceeds 13 months, then internal Decembers play a role of chain-linking months. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Gini, C. (1931). On the Circular Test of Index Numbers. Metron 9:9, 3-24.

Elteto, O., and Koves, P. (1964). On a Problem of Index Number Computation Relating to International Comparisons. Statisztikai Szemle 42, 507-518.

Szulc, B. (1983). Linking Price Index Numbers. In: Price Level Measurement, W. E. Diewert and C. Montmarquette (eds.), 537-566.

Chessa, A.G. (2016). A New Methodology for Processing Scanner Data in the Dutch CPI. Eurona 1/2016, 49-69.

Białek, J. (2022). The general class of multilateral indices and its two special cases. Paper presented at the 17th Meeting of the Ottawa Group on Price Indices, Rome, Italy.

Białek, J. (2022). Improving quality of the scanner CPI: proposition of new multilateral methods, Quality & Quantity, https://doi.org/10.1007/s11135-022-01506-6.

Examples

geksl_fbew(milk, start="2018-12", end="2019-08")

Extending the multilateral GEKS-L price index by using the FBMW method.

Description

This function returns a value of the multilateral GEKS-L price index extended by using the FBMW (Fixed Base Moving Window) method.

Usage

geksl_fbmw(data, start, end)

Arguments

Value

This function returns a value of the multilateral GEKS-L price index (the GEKS index based on the Laspeyres formula) extended by using the FBMW (Fixed Base Moving Window) method. It measures the price dynamics between periods end and start and it uses a 13-month time window with a fixed base month taken as year(end)-1. If the distance between end and start exceeds 13 months, then internal Decembers play a role of chain-linking months. The month of the start parameter must be December. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Gini, C. (1931). On the Circular Test of Index Numbers. Metron 9:9, 3-24.

Elteto, O., and Koves, P. (1964). On a Problem of Index Number Computation Relating to International Comparisons. Statisztikai Szemle 42, 507-518.

Szulc, B. (1983). Linking Price Index Numbers. In: Price Level Measurement, W. E. Diewert and C. Montmarquette (eds.), 537-566.

Lamboray, C.(2017). The Geary Khamis index and the Lehr index: how much do they differ? Paper presented at the 15th Ottawa Group meeting, 10-12 May 2017, Elville am Rhein, Germany.

Białek, J. (2022). The general class of multilateral indices and its two special cases. Paper presented at the 17th Meeting of the Ottawa Group on Price Indices, Rome, Italy.

Białek, J. (2022). Improving quality of the scanner CPI: proposition of new multilateral methods, Quality & Quantity, https://doi.org/10.1007/s11135-022-01506-6.

Examples

geksl_fbmw(milk, start="2019-12", end="2020-04")

Extending the multilateral GEKS-L price index by using window splicing methods.

Description

This function returns a value (or values) of the multilateral GEKS-L price index extended by using window splicing methods. Available splicing methods are: movement splice, window splice, half splice, mean splice and their additional variants: window splice on published indices (WISP), half splice on published indices (HASP) and mean splice on published indices (see References).

Usage

geksl_splice(
  data,
  start,
  end,
  window = 13,
  splice = "movement",
  interval = FALSE
)

Arguments

Value

This function returns a value or values (depending on interval parameter) of the multilateral GEKS-L price index (the GEKS index based on the Laspeyres formula) extended by using window splicing methods. Available splicing methods are: movement splice, window splice, half splice, mean splice and their additional variants: window splice on published indices (WISP), half splice on published indices (HASP) and mean splice on published indices (see References). The time window starts in start and should consist of at least two months. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Chessa, A. G. (2019). A Comparison of Index Extension Methods for Multilateral Methods. Paper presented at the 16th Meeting of the Ottawa Group on Price Indices, 8-10 May 2019, Rio de Janeiro, Brazil.

de Haan, J., van der Grient, H.A. (2011). Eliminating chain drift in price indexes based on scanner data. Journal of Econometrics, 161, 36-46.

Krsinich, F. (2014). The FEWS Index: Fixed Effects with a Window Splice? Non-Revisable Quality-Adjusted Price Indices with No Characteristic Information. Paper presented at the UNECE-ILO Meeting of the Group of Experts on Consumer Price Indices, 2-4 May 2016, Geneva, Switzerland.

de Haan, J.(2015). A Framework for Large Scale Use of Scanner Data in the Dutch CPI. Paper presented at the 14th Ottawa Group meeting, Tokyo, Japan.

Diewert, W.E., and Fox, K.J. (2017). Substitution Bias in Multilateral Methods for CPI Construction using Scanner Data. Discussion paper 17-02, Vancouver School of Economics, The University of British Columbia, Vancouver, Canada.

Białek, J. (2022). The general class of multilateral indices and its two special cases. Paper presented at the 17th Meeting of the Ottawa Group on Price Indices, Rome, Italy.

Białek, J. (2022). Improving quality of the scanner CPI: proposition of new multilateral methods, Quality & Quantity, https://doi.org/10.1007/s11135-022-01506-6.

Examples

geksl_splice(milk, start="2018-12", end="2020-02",splice="half")

Calculating the multilateral GEKS-LM price index

Description

This function returns a value of the multilateral GEKS-LM price index (to be more precise: the GEKS index based on the Lloyd-Moulton price index).

Usage

gekslm(data, start, end, sigma = 0.7, wstart = start, window = 13)

Arguments

Value

This function returns a value of the multilateral GEKS-LM price index (to be more precise: the GEKS index based on the Lloyd-Moulton price index) which considers the time window defined by wstart and window parameters. It measures the price dynamics by comparing period end to period start (both start and end must be inside the considered time window). To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Gini, C. (1931). On the Circular Test of Index Numbers. Metron 9:9, 3-24.

Elteto, O., and Koves, P. (1964). On a Problem of Index Number Computation Relating to International Comparisons. Statisztikai Szemle 42, 507-518.

Szulc, B. (1983). Linking Price Index Numbers. In: Price Level Measurement, W. E. Diewert and C. Montmarquette (eds.), 537-566.

Lloyd, P. J. (1975). Substitution Effects and Biases in Nontrue Price Indices. The American Economic Review, 65, 301-313.

Moulton, B. R. (1996). Constant Elasticity Cost-of-Living Index in Share-Relative Form. Washington DC: U. S. Bureau of Labor Statistics, mimeograph

Examples

gekslm(milk, start="2019-01", end="2019-08",window=10)
gekslm(milk, start="2018-12", end="2019-12", sigma=0.5)

Extending the multilateral GEKS-LM price index by using the FBEW method.

Description

This function returns a value of the multilateral GEKS-LM price index extended by using the FBEW (Fixed Base Monthly Expanding Window) method.

Usage

gekslm_fbew(data, start, end, sigma)

Arguments

Value

This function returns a value of the multilateral GEKS-LM price index extended by using the FBEW (Fixed Base Monthly Expanding Window) method. The FBEW method uses a time window with a fixed base month every year (December). The window is enlarged every month with one month in order to include information from a new month. The full window length (13 months) is reached in December of each year. The function measures the price dynamics between periods end and start. The month of the start parameter must be December. If the distance between end and start exceeds 13 months, then internal Decembers play a role of chain-linking months. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).

References

Gini, C. (1931). On the Circular Test of Index Numbers. Metron 9:9, 3-24.

Elteto, O., and Koves, P. (1964). On a Problem of Index Number Computation Relating to International Comparisons. Statisztikai Szemle 42, 507-518.

Szulc, B. (1983). Linking Price Index Numbers. In: Price Level Measurement, W. E. Diewert and C. Montmarquette (eds.), 537-566.

Chessa, A.G. (2016). A New Methodology for Processing Scanner Data in the Dutch CPI. Eurona 1/2016, 49-69.

Examples

gekslm_fbew(milk, start="2018-12", end="2019-08", sigma=1.2)

Extending the multilateral GEKS-LM price index by using the FBMW method.

Description

This function returns a value of the multilateral GEKS-LM price index extended by using the FBMW (Fixed Base Moving Window) method.

Usage

gekslm_fbmw(data, start, end, sigma)

Arguments

Value

This function returns a value of the multilateral GEKS-LM price index extended by using the FBMW (Fixed Base Moving Window) method. It measures the price dynamics between periods end and start and it uses a 13-month time window with a fixed base month taken as year(end)-1. If the distance between end and start exceeds 13 months, then internal Decembers play a role of chain-linking months. The month of the start parameter must be December. To get information about both price index values and corresponding dates, please see functions: price_indices or final_index. The function does not take into account aggregating over outlets or product subgroups (to consider these types of aggregating, please use the final_index function).