Help for package factorstochvol

Encoding:

UTF-8

Type:

Package

Title:

Bayesian Estimation of (Sparse) Latent Factor Stochastic Volatility Models

Version:

1.1.0

Description:

Markov chain Monte Carlo (MCMC) sampler for fully Bayesian estimation of latent factor stochastic volatility models with interweaving <doi:10.1080/10618600.2017.1322091>. Sparsity can be achieved through the usage of Normal-Gamma priors on the factor loading matrix <doi:10.1016/j.jeconom.2018.11.007>.

License:

GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]

Depends:

R (≥ 3.0.2)

Imports:

GIGrvg (≥ 0.4), Rcpp (≥ 1.0.0), corrplot, methods, grDevices, graphics, stats, utils, stochvol (≥ 3.0.2)

Suggests:

LSD (≥ 4.0-0), coda (≥ 0.19-2), knitr, RColorBrewer, testthat (≥ 2.1.0), zoo

LinkingTo:

Rcpp, RcppArmadillo (≥ 0.9.900), stochvol

RoxygenNote:

7.2.3

BuildResaveData:

best

VignetteBuilder:

knitr

NeedsCompilation:

yes

Packaged:

2023-11-24 09:49:56 UTC; grkastner

Author:

Gregor Kastner

[aut, cre], Darjus Hosszejni

[ctb], Luis Gruber

[ctb]

Maintainer:

Gregor Kastner <gregor.kastner@aau.at>

Repository:

CRAN

Date/Publication:

2023-11-24 11:30:11 UTC

Bayesian Estimation of (Sparse) Latent Factor Stochastic Volatility Models through MCMC

Description

This packages provides a Markov chain Monte Carlo (MCMC) sampler for fully Bayesian estimation of latent factor stochastic volatility models. Sparsity can be achieved through the usage of Normal-Gamma priors on the factor loadings matrix.

Details

In recent years, multivariate factor stochastic volatility (SV) models have been increasingly used to analyze financial and economic time series because they can capture joint (co-)volatility dynamics by a small number of latent time-varying factors. The main advantage of such a model is its parsimony, as all variances and covariances of a time series vector are governed by a low-dimensional common factor with the components following independent SV models. For problems of this kind, MCMC is a very efficient estimation method, it is however associated with a considerable computational burden when the number of assets is moderate to large. To overcome this, the latent volatility states are drawn "all without a loop" (AWOL), ancillarity-sufficiency interweaving strategies (ASIS) are applied to sample the univariate components as well as the factor loadings. Thus, this package can be applied directly estimate time-varying covariance and correlation matrices for medium-and high-dimensional time series. To guarantee sparsity, a hierarchical Normal-Gamma prior can be used for the factor loadings matrix which shrinks the unnecessary factor loadings towards zero.

Note

This package is currently in active development; the interface of some of the functions might change. Moreover, even though I tried to carefully check everything, factorstochvol may still contain typos, inconsistencies, or even bugs. Your comments and suggestions are warmly welcome!

Author(s)

Gregor Kastner gregor.kastner@wu.ac.at

References

Kastner, G., Frühwirth-Schnatter, S., and Lopes, H.F. (2017). Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models. Journal of Computational and Graphical Statistics, 26(4), 905–917, doi:10.1080/10618600.2017.1322091.

Kastner, G. (2019). Sparse Bayesian Time-Varying Covariance Estimation in Many Dimensions. Journal of Econometrics, 210(1), 98–115. doi:10.1016/j.jeconom.2018.11.007.

Kastner, G. and Frühwirth-Schnatter, S. (2014). Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Estimation of Stochastic Volatility Models. Computational Statistics and Data Analysis, doi:10.1016/j.csda.2013.01.002.

Examples


set.seed(1)

# simulate data from a (small) factor SV model:
sim <- fsvsim(series = 5, factors = 2)

# estimate the model (CAVEAT: only few draws!)
res <- fsvsample(sim$y, factors = 2, draws = 2000, burnin = 500)

# plot implied volas overtime:
voltimeplot(res)

# plot correlation matrix at some points in time:
par(mfrow = c(2,2))
corimageplot(res, seq(1, nrow(sim$y), length.out = 4),
             fsvsimobj = sim, plotCI = 'circle',
             plotdatedist = -2)


# plot (certain) covariances and correlations over time
par(mfrow = c(2,1))
covtimeplot(res, 1)
cortimeplot(res, 1)

# plot (all) correlations over time
corplot(res, fsvsimobj = sim, these = 1:10)

# plot factor loadings
par(mfrow = c(1,1))
facloadpointplot(res, fsvsimobj = sim)
facloadpairplot(res)
facloadcredplot(res)
facloaddensplot(res, fsvsimobj = sim)

# plot latent log variances
logvartimeplot(res, fsvsimobj = sim, show = "fac")
logvartimeplot(res, fsvsimobj = sim, show = "idi")

# plot communalities over time
comtimeplot(res, fsvsimobj = sim, show = 'joint')
comtimeplot(res, fsvsimobj = sim, show = 'series')

Plot communalities over time.

Description

comtimeplot plots the communalities over time, i.e. the series-specific percentage of variance explained through the common factors.

Usage

comtimeplot(
  x,
  fsvsimobj = NULL,
  show = "series",
  maxrows = 5,
  ylim = c(0, 100)
)

Arguments

x

Object of class 'fsvdraws', usually resulting from a call to fsvsample.

fsvsimobj

Object of class 'fsvsim' (or NULL), usually resulting from a call to fsvsim. Defaults to NULL.

show

Indicator whether to show joint ('joint'), series-specific ('series'), or both ('both') communalities.

maxrows

Single positive integer denoting the maximum number of series in each plot. Defaults to 5.

ylim

Vector of length two denoting the range of the horizontal axis. Defaults to 1.

Details

This function displays the joint (average) communalities over time and all series-specific communalities. If communalities haven't been stored during sampling, comtimeplot produces an error.

Value

Returns x invisibly.

Extract "true" model-implied correlations of two series only

Description

corelement extracts the model-implied (time-varying) correlations between (exactly) two component series.

Usage

corelement(x, i, j, these = seq_len(nrow(x$y)))

Arguments

x

Object of class 'fsvsim', usually resulting from a call of the function fsvsim.

i

Index of component series 1.

j

Index of component series 2.

these

Vector indicating which points in time should be extracted.

Value

Vector with the requested correlations.

Plot correlation matrices for certain points in time

Description

corimageplot plots the model-implied correlation matrices for one or several points in time.

Usage

corimageplot(
  x,
  these = seq_len(nrow(x$y)),
  order = "original",
  these4order = these,
  plotdatedist = 0,
  plotCI = "n",
  date.cex = 1.5,
  col = NULL,
  fsvsimobj = NULL,
  plottype = "corrplot",
  ...
)

Arguments

x

Object of class 'fsvdraws', usually resulting from a call to fsvsample.

these

Index vector containing the time points to plot. Defaults to seq_len(nrow(x$y)).

order

String, where 'none' and 'original' indicate not to mess with the series ordering. Other keywords (e.g. 'hclust') will be forwarded to corrMatOrder.

these4order

Index vector containing the time points used for ordering. Probably, the default (these) is what you want.

plotdatedist

Numerical value indicating where the dates should be plotted.

plotCI

String. If not equal to 'n', posterior credible regions are added (posterior mean +/- 2 posterior sd). Ignored if plottype is "imageplot".

date.cex

Size multiplier for the dates.

col

Color palette or NULL (the default).

fsvsimobj

To indicate data generating values in case of simulated data, pass an object of type fsvsim (usually the result of a call to fsvsim).

plottype

Indicates which type of plot should be drawn. Can be "corrplot" for corrplot (recommended for up to around 20 series), or "imageplot" for a simpler image plot.

...

Additional parameters will be passed on to corrplot. Ignored if plottype is "imageplot".

Value

Returns x invisibly.

Note

If correlations haven't been stored during sampling, corimageplot produces an error.

Generic extraction of correlation matrix

Description

Generic function for extracting model-implied correlation matrices, either from the MCMC output, or from the simulated model. Details about the function's behavior can be found in cormat.fsvdraws (the function invoked when applied to MCMC output) or cormat.fsvsim (the function invoked when applied to a simulated model.

Usage

cormat(x, ...)

Arguments

x

An object of class fsvdraws or fsvsim.

...

Arguments to be passed to methods.

Value

Structure containing the model-implied covariance matrix.

Extract posterior draws of the model-implied correlation matrix

Description

cormat extracts draws from the model-implied correlation matrix from an fsvdraws object for all points in time which have been stored.

Usage

## S3 method for class 'fsvdraws'
cormat(x, timepoints = "all", ...)

Arguments

x

Object of class 'fsvdraws', usually resulting from a call of fsvsample.

timepoints

Vector indicating at which point(s) in time (of those that have been stored during sampling) the correlation matrices should be extracted. Can also be "all" or "last".

...

Ignored.

Value

Array of dimension m times m times draws times timepoints containing the posterior draws for the model-implied covariance matrix.

Note

Currently crudely implemented as a double loop in pure R, may be slow.

Examples


set.seed(1)
sim <- fsvsim(n = 500, series = 3, factors = 1) # simulate
res <- fsvsample(sim$y, factors = 1, keeptime = "all") # estimate
cors <- cormat(res, "last") # extract

# Trace plot of determinant of posterior correlation matrix
# at time t = n = 500:
detdraws <- apply(cors[,,,1], 3, det)
ts.plot(detdraws)
abline(h = mean(detdraws), col = 2)          # posterior mean
abline(h = median(detdraws), col = 4)        # posterior median
abline(h = det(cormat(sim, "last")[,,1]), col = 3) # implied by DGP

# Trace plot of draws from posterior correlation of Sim1 and Sim2 at
# time t = n = 500:
ts.plot(cors[1,2,,1])
abline(h = cormat(sim, "last")[1,2,1], col = 3) # "true" value

# Smoothed kernel density estimate:
plot(density(cors[1,2,,1], adjust = 2))

# Summary statistics:
summary(cors[1,2,,1])

Extract "true" model-implied correlation matrix for several points in time

Description

cormat extracts the model-implied (time-varying) covariance matrix from an fsvsim object.

Usage

## S3 method for class 'fsvsim'
cormat(x, timepoints = "all", ...)

Arguments

x

Object of class 'fsvsim', usually resulting from a call of the function fsvsim.

timepoints

Vector indicating at which point(s) in time the correlation matrices should be extracted. Can also be "all" or "last".

...

Ignored.

Value

Array of dimension m times m times length(timepoints), containing the model-implied correlation matrix.

Note

Currently crudely implemented as an R loop over all time points, may be slow.

Plots pairwise correlations over time

Description

corplot gives an overview of (certain) pairwise correlations. Throws a warning if these haven't been stored during sampling.

Usage

corplot(
  x,
  fsvsimobj = NULL,
  these = 1:(ncol(x$y) * (ncol(x$y) - 1)/2),
  start = 1,
  end = nrow(x$y),
  maxrows = 10,
  ...
)

Arguments

x

Object of class 'fsvdraws', usually resulting from a call to fsvsample.

fsvsimobj

To indicate data generating values in case of simulated data, pass an object of type fsvsim (usually the result of a call to fsvsim).

these

Indicator which correlations should be plotted. Default is all.

start

First point in time to plot.

end

Last point in time to plot.

maxrows

The maximum number of rows per page.

...

Other arguments will be passed on to ts.plot.

Value

Returns x invisibly.

Plot correlations over time.

Description

cortimeplot draws correlations over time.

Usage

cortimeplot(
  x,
  series,
  these = seq_len(nrow(x$y)),
  type = "cor",
  statistic = "mean"
)

covtimeplot(
  x,
  series,
  these = seq_len(nrow(x$y)),
  type = "cov",
  statistic = "mean"
)

Arguments

x

Object of class 'fsvdraws', usually resulting from a call to fsvsample.

series

Single number, coercible to integer. Indicates the series relative to which correlations are drawn.

these

Index vector containing the time points to plot. Defaults to seq_len(nrow(x$y)).

type

What to plot, usually "cor" or "cov".

statistic

Which posterior summary should be plotted, usually "mean".

Details

This function displays one component series' time-varying correlations with the other components series. Throws an error if correlations haven't been stored during sampling.

Value

Returns x invisibly.

Extract "true" model-implied covariances of two series only

Description

covelement extracts the model-implied (time-varying) covariances between (exactly) two component series.

Usage

covelement(x, i, j, these = seq_len(nrow(x$y)))

Arguments

x

Object of class 'fsvsim', usually resulting from a call of the function fsvsim.

i

Index of component series 1.

j

Index of component series 2.

these

Vector indicating which points in time should be extracted, defaults to all.

Value

Vector with the requested covariances.

Generic extraction of covariance matrix

Description

Generic function for extracting model-implied covariance matrices, either from the MCMC output, or from the simulated model. Details about the function's behavior can be found in covmat.fsvdraws (the function invoked when applied to MCMC output) or covmat.fsvsim (the function invoked when applied to a simulated model.

Usage

covmat(x, ...)

Arguments

x

An object of class fsvdraws or fsvsim.

...

Arguments to be passed to methods.

Value

Structure containing the model-implied covariance matrix.

Extract posterior draws of the model-implied covariance matrix

Description

covmat extracts draws from the model-implied covariance matrix from an fsvdraws object for all points in time which have been stored.

Usage

## S3 method for class 'fsvdraws'
covmat(x, timepoints = "all", ...)

Arguments

x

Object of class 'fsvdraws', usually resulting from a call of fsvsample.

timepoints

Vector indicating at which point(s) in time (of those that have been stored during sampling) the correlation matrices should be extracted. Can also be "all" or "last".

...

Ignored.

Value

Array of dimension m times m times draws times timepoints containing the posterior draws for the model-implied covariance matrix.

Note

Currently crudely implemented as a double loop in pure R, may be slow.

Examples


set.seed(1)
sim <- fsvsim(n = 500, series = 3, factors = 1) # simulate
res <- fsvsample(sim$y, factors = 1, keeptime = "all") # estimate
covs <- covmat(res, "last") # extract

# Trace plot of determinant of posterior covariance matrix
# at time t = n = 500:
detdraws <- apply(covs[,,,1], 3, det)
ts.plot(detdraws)
abline(h = mean(detdraws), col = 2)          # posterior mean
abline(h = median(detdraws), col = 4)        # posterior median
abline(h = det(covmat(sim, "last")[,,1]), col = 3) # implied by DGP

# Trace plot of draws from posterior covariance of Sim1 and Sim2 at
# time t = n = 500:
ts.plot(covs[1,2,,1])
abline(h = covmat(sim, "last")[1,2,1], col = 3) # "true" value

# Smoothed kernel density estimate:
plot(density(covs[1,2,,1], adjust = 2))

# Summary statistics:
summary(covs[1,2,,1])

Extract "true" model-implied covariance matrix for several points in time

Description

covmat extracts the model-implied (time-varying) covariance matrix from an fsvsim object.

Usage

## S3 method for class 'fsvsim'
covmat(x, timepoints = "all", ...)

Arguments

x

Object of class 'fsvsim', usually resulting from a call of the function fsvsim.

timepoints

Vector indicating at which point(s) in time the correlation matrices should be extracted. Can also be "all" or "last".

...

Ignored.

Value

Array of dimension m times m times length(timepoints), containing the model-implied covariance matrix.

Note

Currently crudely implemented as an R loop over all time points, may be slow.

Plots posterior draws and posterior means of the eigenvalues of crossprod(facload)

Description

evdiag computes, returns, and visualizes the eigenvalues of crossprod(facload). This can be used as a rough guide to choose the numbers of factors in a model.

Usage

evdiag(x)

Arguments

x

Object of class 'fsvdraws', usually resulting from a call to fsvsample.

Value

Invisibly returns a matrix with posterior samples of the eigenvalues of crossprod(facload)

Note

Experimental feature. Please be aware that - for the sake of simplicity and interpretability - both the time-varying idiosyncratic as well as the time-varying factor volatilities are simply ignored.

Computes the empirical exponentially weighted covariance matrix

Description

A common way to get estimates for time-varying covariance matrices is the compute the exponentially weighted empirical covariance matrix.

Usage

expweightcov(dat, alpha = 4/126, hist = 180)

Arguments

dat

Matrix containing the data, with n rows (points in time) and m columns (component series).

alpha

Speed of decay.

hist

How far to go back in time?

Value

A m times m covariance matrix estimate.

Displays bivariate marginal posterior distribution of factor loadings.

Description

facloadcredplot illustrates the bivariate marginals of the factor loadings distribution. It is a monochrome variant of facloadpairplot.

Usage

facloadcredplot(x, quants = c(0.01, 0.99))

Arguments

x

Object of class 'fsvdraws', usually resulting from a call to fsvsample.

quants

Posterior quantiles to be plotted.

Value

Returns x invisibly.

Density plots of factor loadings draws

Description

facloaddensplot draws kernel smoothed density plots of the marginal factor loadings posterior.

Usage

facloaddensplot(x, fsvsimobj = NULL, rows = 5, thesecols = NULL, xlim = NULL)

Arguments

x

Object of class 'fsvdraws', usually resulting from a call to fsvsample.

fsvsimobj

To indicate data generating values in case of simulated data, pass an object of type fsvsim (usually the result of a call to fsvsim).

rows

Number of rows per page.

thesecols

Which factor loadings columns should be plotted? Defaults to 1:r.

xlim

Vector of length two containing lower and upper bounds of the horizontal axis. If NULL, these are automatically determined.

Value

Returns x invisibly.

Displays bivariate marginal posterior distributions of factor loadings.

Description

facloadpairplot illustrates the bivariate marginals of the factor loadings distribution. For a monochrome variant, see facloadcredplot.

Usage

facloadpairplot(x, maxpoints = 500, alpha = 20/maxpoints, cex = 3)

Arguments

x

Object of class 'fsvdraws', usually resulting from a call to fsvsample.

maxpoints

The maximum amount of posterior draws to plot. If the number of draws stored in x exceeds this number, draws are thinned accordingly.

alpha

Level of transparency.

cex

Controls the size of the dots.

Value

Returns x invisibly.

Displays point estimates of the factor loadings posterior.

Description

facloadpointplot illustrates point estimates (mean, median, ...) of the estimated factor loadings matrix.

Usage

facloadpointplot(
  x,
  fsvsimobj = NULL,
  statistic = "median",
  cex = 6.5,
  alpha = 0.2,
  allpairs = FALSE,
  col = NULL
)

Arguments

x

Object of class 'fsvdraws', usually resulting from a call to fsvsample.

fsvsimobj

To indicate data generating values in case of simulated data, pass an object of type fsvsim (usually the result of a call to fsvsim).

statistic

Character string indicating which posterior statistic should be displayed.

cex

Controls the size of the dots.

alpha

Controls the level of transparency.

allpairs

Logical value; if set to TRUE, all possible pairwise combinations will be plotted.

col

Vector of length m (number of component series), containing rgb-type color codes used for plotting. Will be recycled if necessary.

Value

Returns x invisibly, throws a warning if there aren't any factors to plot.

Trace plots of factor loadings draws

Description

facloadtraceplot draws trace plots of the factor loadings. Can be an important tool to check MCMC convergence if inference about (certain) factor loadings sought.

Usage

facloadtraceplot(
  x,
  fsvsimobj = NULL,
  thinning = NULL,
  maxrows = 10,
  ylim = NULL
)

Arguments

x

Object of class 'fsvdraws', usually resulting from a call to fsvsample.

fsvsimobj

To indicate data generating values in case of simulated data, pass an object of type fsvsim (usually the result of a call to fsvsim).

thinning

Plot every thinningth draw.

maxrows

Indicates the maximum number of rows to be drawn per page.

ylim

Vector of length two containing lower and upper bounds of the vertical axis. If NULL, these are automatically determined.

Value

Returns x invisibly.

Ad-hoc method for (weakly) identifying the factor loadings matrix

Description

In factor SV models, the identification of the factor loadings matrix is often chosen through a preliminary static factor analysis. After a maximum likelihood factor model is fit to the data, variables are ordered as follows: The variable with the lowest loadings on all factors except the first (relative to it) is determined to lead the first factor, the variable with the lowest loadings on all factors except the first two (relative to these) is determined to lead the second factor, etc.

Usage

findrestrict(dat, factors, transload = abs, relto = "all")

Arguments

dat

Matrix containing the data, with n rows (points in time) and m columns (component series).

factors

Number of factors to be used.

transload

Function for transforming the estimated factor loadings before ordering. Defaults to the absolute value function.

relto

Can be 'none', 'current' or 'all'. If 'none', the series with the highest loadings is placed first, the series with the second highest is placed second, and so on. If 'current', the current factor loading is used as a reference, if 'all', all previous loadings are summed up to be the reference.

Value

A m times factors matrix indicating the restrictions.

Note

This function is automatically invoked by fsvsample if restrict is set to 'auto'.

Markov Chain Monte Carlo (MCMC) Sampling for the Factor Stochastic Volatility Model.

Description

fsvsample simulates from the joint posterior distribution and returns the MCMC draws. It is the main workhorse to conduct inference for factor stochastic volatility models in this package.

Usage

fsvsample(
  y,
  factors = 1,
  draws = 1000,
  thin = 1,
  burnin = 1000,
  restrict = "none",
  zeromean = TRUE,
  priorfacloadtype = "rowwiseng",
  priorfacload = 0.1,
  facloadtol = 1e-18,
  priorng = c(1, 1),
  priormu = c(0, 10),
  priorphiidi = c(10, 3),
  priorphifac = c(10, 3),
  priorsigmaidi = 1,
  priorsigmafac = 1,
  priorh0idi = "stationary",
  priorh0fac = "stationary",
  priorbeta = c(0, 10000),
  keeptime = "last",
  heteroskedastic = TRUE,
  priorhomoskedastic = NA,
  runningstore = 6,
  runningstorethin = 10,
  runningstoremoments = 2,
  signident = TRUE,
  signswitch = FALSE,
  interweaving = 4,
  quiet = FALSE,
  samplefac = TRUE,
  startfac,
  startpara,
  startlogvar,
  startlatent,
  startlogvar0,
  startlatent0,
  startfacload,
  startfacloadvar,
  expert
)

Arguments

y

Data matrix. Each of m columns is assumed to contain a single (univariate) series of length n.

factors

Number of latent factors to be estimated.

draws

Number of MCMC draws kept after burn-in.

thin

Single number greater or equal to 1, coercible to integer. Every thinth MCMC draw is kept and returned. The default value is 1, corresponding to no thinning of the draws, i.e. every draw is stored.

burnin

Number of initial MCMC draws to be discarded.

restrict

Either "upper", "none", or "auto", indicating whether the factor loadings matrix should be restricted to have zeros above the diagonal ("upper"), whether all elements should be estimated from the data ("none"), or whether the function findrestrict should be invoked for a priori finding suitable zeros. Setting restrict to "upper" or "auto" often stabilizes MCMC estimation and can be important for identifying the factor loadings matrix, however, it generally is a strong prior assumption. Setting restrict to "none" is usually the preferred option if identification of the factor loadings matrix is of less concern but covariance estimation or prediction is the goal. Alternatively, restrict can be a logical matrix of dimension c(m, r) indicating which elements should be unrestricted (where restrict is FALSE) or zero (where restrict is TRUE).

zeromean

Logical. If FALSE, a constant mean is included in the model for each of the m univariate series. If TRUE, the mean is not modeled. Defaults to TRUE.

priorfacloadtype

Can be "normal", "rowwiseng", "colwiseng".

"normal":: Normal prior. The value of priorfacload is interpreted as the standard deviations of the Gaussian prior distributions for the factor loadings.
"rowwiseng":: Row-wise Normal-Gamma prior. The value of priorfacload is interpreted as the shrinkage parameter a.
"colwiseng":: Column-wise Normal-Gamma prior. The value of priorfacload is interpreted as the shrinkage parameter a.

For details please see Kastner (2019).

priorfacload

Either a matrix of dimensions m times factors with positive elements or a single number (which will be recycled accordingly). The meaning of priorfacload depends on the setting of priorfacloadtype and is explained there.

facloadtol

Minimum number that the absolute value of a factor loadings draw can take. Prevents numerical issues that can appear when strong shrinkage is enforced if chosen to be greater than zero.

priorng

Two-element vector with positive entries indicating the Normal-Gamma prior's hyperhyperparameters c and d.

priormu

Vector of length 2 denoting prior mean and standard deviation for unconditional levels of the idiosyncratic log variance processes.

priorphiidi

Vector of length 2, indicating the shape parameters for the Beta prior distributions of the transformed parameters (phi+1)/2, where phi denotes the persistence of the idiosyncratic log variances.

priorphifac

Vector of length 2, indicating the shape parameters for the Beta prior distributions of the transformed parameters (phi+1)/2, where phi denotes the persistence of the factor log variances.

priorsigmaidi

Vector of length m containing the prior volatilities of log variances. If priorsigmaidi has exactly one element, it will be recycled for all idiosyncratic log variances.

priorsigmafac

Vector of length factors containing the prior volatilities of log variances. If priorsigmafac has exactly one element, it will be recycled for all factor log variances.

priorh0idi

Vector of length 1 or m, containing information about the Gaussian prior for the initial idiosyncratic log variances. If an element of priorh0idi is a nonnegative number, the conditional prior of the corresponding initial log variance h0 is assumed to be Gaussian with mean 0 and standard deviation priorh0idi times $sigma$. If an element of priorh0idi is the string 'stationary', the prior of the corresponding initial log volatility is taken to be from the stationary distribution, i.e. h0 is assumed to be Gaussian with mean 0 and variance $sigma^2/(1-phi^2)$.

priorh0fac

Vector of length 1 or factors, containing information about the Gaussian prior for the initial factor log variances. If an element of priorh0fac is a nonnegative number, the conditional prior of the corresponding initial log variance h0 is assumed to be Gaussian with mean 0 and standard deviation priorh0fac times $sigma$. If an element of priorh0fac is the string 'stationary', the prior of the corresponding initial log volatility is taken to be from the stationary distribution, i.e. h0 is assumed to be Gaussian with mean 0 and variance $sigma^2/(1-phi^2)$.

priorbeta

numeric vector of length 2, indicating the mean and standard deviation of the Gaussian prior for the regression parameters. The default value is c(0, 10000), which constitutes a very vague prior for many common datasets. Not used if zeromean is TRUE.

keeptime

Either a number coercible to a positive integer, or a string equal to "all" or "last". If a number different from 1 is provided, only every keeptimeth latent log-volatility is being monitored. If, e.g., keeptime = 3, draws for the latent log variances h_1,h_4,h_7,... will be kept. If keeptime is set to "all", this is equivalent to setting it to 1. If keeptime is set to "last" (the default), only draws for the very last latent log variances h_n are kept.

heteroskedastic

Vector of length 1, 2, or m + factors, containing logical values indicating whether time-varying (heteroskedastic = TRUE) or constant (heteroskedastic = FALSE) variance should be estimated. If heteroskedastic is of length 2 it will be recycled accordingly, whereby the first element is used for all idiosyncratic variances and the second element is used for all factor variances.

priorhomoskedastic

Only used if at least one element of heteroskedastic is set to FALSE. In that case, priorhomoskedastic must be a matrix with positive entries and dimension c(m, 2). Values in column 1 will be interpreted as the shape and values in column 2 will be interpreted as the rate parameter of the corresponding inverse gamma prior distribution of the idisyncratic variances.

runningstore

Because most machines these days do not have enough memory to store all draws for all points in time, setting runningstore to an integer greater than 0 will cause fsvsample to store the first runningstoremoments ergodic moments of certain variables of interest. More specifically, mean, variance, skewness, etc. will be stored for certain variables if runningstore is set to a value...

>= 1:: Latent log variances h_1,h_2,...,h_(n+r).
>= 2:: Latent factors f_1,...,f_r.
>= 3:: Latent volatilities sqrt(exp(h_1,h_2,...,h_(n+r))).
>= 4:: Conditional covariance matrix and the square roots of its diagonal elements.
>= 5:: Conditional correlation matrix.
>= 6:: Communalities, i.e. proportions of variances explained through the common factors.

runningstorethin

How often should the calculation of running moments be conducted? Set to a value > 1 if you want to avoid time consuming calculations at every MCMC iteration.

runningstoremoments

Selects how many running moments (up to 4) should be calculated.

signident

If set to FALSE, no ex-post sign-identification is performed. Defaults to TRUE.

signswitch

Set to TRUE to turn on a random sign switch of factors and loadings. Note that the signs of each factor loadings matrix column and the corresponding factor cannot be identified from the likelihood.

interweaving

The following values for interweaving the factor loadings are accepted:

0:: No interweaving.
1:: Shallow interweaving through the diagonal entries.
2:: Deep interweaving through the diagonal entries.
3:: Shallow interweaving through the largest absolute entries in each column.
4:: Deep interweaving through the largest absolute entries in each column.

For details please see Kastner et al. (2017). A value of 4 is the highly recommended default.

quiet

Logical value indicating whether the progress bar and other informative output during sampling should be omitted. The default value is FALSE, implying verbose output.

samplefac

If set to FALSE, the factors are not sampled (but remain at their starting values forever). This might be useful if one wants to include observed factors instead of latent ones.

startfac

optional numeric matrix of dimension c(factors, n), containing the starting values of the latent factors. In case of a single factor model, a numeric vector of length n is also accepted.

startpara

optional numeric matrix of dimension c(3, m + factors), containing the starting values for the parameter draws. The first m columns must contain parameters values corresponding to the idiosyncratic volatilities, the subsequent factor columns must contain parameter values corresponding to the factor volatilities. The first row of startpara corresponds to mu, the level of the log variances (can be arbitrary numerical values), the second row corresponds to phi, the persistence parameters of the log variances (numeric values between -1 and 1), and the third row corresponds to sigma (positive numeric values).

startlogvar

optional numeric matrix of dimension c(n, m + factors), containing the starting values of the latent log variances. The first m rows correspond to the idiosyncratic log variances, the subsequent factor rows correspond to the factor log variances. Was previously called startlatent.

startlatent

Deprecated. Please use startlogvar instead.

startlogvar0

optional numeric vector of length m + factors, containing the starting values of the initial latent log variances. The first m elements correspond to the idiosyncratic log variances, the subsequent factor elements correspond to the factor log variances. Was previously called startlatent0.

startlatent0

Deprecated. Please use startlogvar0 instead.

startfacload

optional numeric matrix of dimension c(m, factors), containing the starting values of the factor loadings. In case of a single factor model, a numeric vector of length n is also accepted.

startfacloadvar

optional numeric matrix of dimension c(m, factors), containing the starting values of the factor loadings variances \tau_{ij}^2. Used only when the normal-gamma prior is employed (priorfacloadtype != "normal") while ignored when static loadings variances are used (priorfacloadtype == "normal").

expert

optional named list of expert parameters for the univariate SV models (will be transformed and passed to the stochvol package). For most applications, the default values probably work best. Interested users are referred to Kastner and Frühwirth-Schnatter (2014), the package vignette, and Kastner (2016). If expert is provided, it may contain the following named elements:

parameterization:: Character string equal to "centered", "noncentered", "GIS_C", or "GIS_NC". Defaults to "GIS_C".
mhcontrol:: Single numeric value controlling the proposal density of a Metropolis-Hastings (MH) update step when sampling sigma. If mhcontrol is smaller than 0, an independence proposal will be used, while values greater than zero control the stepsize of a log-random-walk proposal. Defaults to -1.
gammaprior:: Single logical value indicating whether a Gamma prior for sigma^2 should be used. If set to FALSE, an Inverse Gamma prior is employed. Defaults to TRUE.
truncnormal:: Single logical value indicating whether a truncated Gaussian distribution should be used as proposal for draws of phi. If set to FALSE, a regular Gaussian prior is employed and the draw is immediately discarded when values outside the unit ball happen to be drawn. Defaults to FALSE.
mhsteps:: Either 1, 2, or 3. Indicates the number of blocks used for drawing from the posterior of the parameters. Defaults to 2.
proposalvar4sigmaphi:: Single positive number indicating the conditional prior variance of sigma*phi in the ridge proposal density for sampling (mu, phi). Defaults to 10^8.
proposalvar4sigmatheta:: Single positive number indicating the conditional prior variance of sigma*theta in the ridge proposal density for sampling (mu, phi). Defaults to 10^12.

Details

For details concerning the factor SV algorithm please see Kastner et al. (2017), details about the univariate SV estimation can be found in Kastner and Frühwirth-Schnatter (2014).

Value

The value returned is a list object of class fsvdraws holding

facload:: Array containing draws from the posterior distribution of the factor loadings matrix.
fac:: Array containing factor draws from the posterior distribution.
logvar:: Array containing idiosyncratic and factor initial log variance draws.
logvar0:: Array containing idiosyncratic and factor log variance draws.
para:: Array containing parameter draws form the posterior distribution.
y:: Matrix containing the data supplied.
latestauxiliary:: List containing the latest draws of auxiliary quantities used for sampling the factor loadings matrix.
runningstore:: List whose elements contain ergodic moments of certain variables of interest. See argument runningstore for details about what is being stored here.
config:: List containing information on configuration parameters.
priors:: List containing prior hyperparameter values.
identifier:: Matrix containing the indices of the series used for ex-post sign-identification along with the corresponding minimum distances to zero. See signident for details.

To display the output, use print, plot, and in particular specialized extractors and printing functions. The print method prints a high-level overview; specialized extractors such as covmat or runningcovmat are also available. The plot method invokes a simple covariance matrix plot; specialized plotting functions are linked in the documentation of plot.fsvdraws.

References

Kastner, G. (2019). Sparse Bayesian Time-Varying Covariance Estimation in Many Dimensions Journal of Econometrics, 210(1), 98–115, doi:10.1016/j.jeconom.2018.11.007

Kastner, G. (2016). Dealing with stochastic volatility in time series using the R package stochvol. Journal of Statistical Software, 69(5), 1–30, doi:10.18637/jss.v069.i05.

Kastner, G. and Frühwirth-Schnatter, S. (2014). Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Estimation of Stochastic Volatility Models. Computational Statistics & Data Analysis, 76, 408–423, doi:10.1016/j.csda.2013.01.002.

Examples


# Load exchange rate data (ships with stochvol):
data(exrates, package = "stochvol")
exrates$date <- NULL

# Compute the percentage log returns:
dat <- 100 * logret(exrates)

# We are going to fit a one-factor model so the ordering is irrelevant
# NOTE that these are very few draws, you probably want more...
res <- fsvsample(dat, factors = 2, draws = 2000, burnin = 1000,
  runningstore = 6, zeromean = FALSE)

voltimeplot(res)

corimageplot(res, nrow(dat), plotCI = 'circle')

oldpar <- par(ask = TRUE)
plot(res)
par(oldpar)
pairs(t(res$beta[1:4, ]))

Simulate data from a factor SV model

Description

fsvsim generates simulated data from a factor SV model.

Usage

fsvsim(
  n = 1000,
  series = 10,
  factors = 1,
  facload = "dense",
  idipara,
  facpara,
  heteroskedastic = rep(TRUE, series + factors),
  df = Inf
)

Arguments

n

Length of the series to be generated.

series

Number of component series m.

factors

Number of factors r.

facload

Can either be a matrix of dimension m times r or one of the keywords "dense" and "sparse". If "dense" is chosen, a (rather) dense lower triangular factor loadings matrix is randomly generated. If "sparse" is chosen, a (rather) sparse lower triangular factor loadings matrix is randomly generated.

idipara

Optional matrix of idiosyncratic SV parameters to be used for simulation. Must have exactly three columns containing the values of mu, phi and sigma for each of m series, respectively. If omitted, plausible values are generated.

facpara

Optional matrix of idiosyncratic SV parameters to be used for simulation. Must have exactly two columns containing the values of phi and sigma for each of r factors, respectively. If omitted, plausible values are generated.

heteroskedastic

Logical vector of length m+r. When TRUE, time-varying volatilities are generated; when FALSE, constant volatilities (equal to mu) are generated.

df

If not equal to Inf, the factors are misspecified (come from a t distribution instead of a Gaussian). Only used for testing.

Value

The value returned is a list object of class fsvsim holding

y: The simulated data, stored in a n times m matrix with colnames 'Sim1', 'Sim2', etc.
fac: The simulated factors, stored in a r times r matrix.
facload: Factor loadings matrix.
facvol: Latent factor log-variances for times 1 to n.
facvol0: Initial factor log-variances for time 0.
facpara: The parameters of the factor volatility processes.
idivol: Latent idiosyncratic log-variances for times 1 to n.
idivol0: Initial idiosyncratic log-variances for time 0.
idipara: The parameters of the idiosyncratic volatility processes.

Note

This object can be passed to many plotting functions to indicate the data generating processes when visualizing results.

Ledermann bound for the number of factors

Description

In the static factor case, the Ledermann bound is the largest integer rank for which a unique decomposition of the covariance matrix is possible. (This is the largest possible number of factors which can be used for factanal.

Usage

ledermann(m)

Arguments

m

Number of component series.

Value

The Ledermann bound, a nonnegative integer.

Compute the log returns of a vector-valued time series

Description

logret computes the log returns of a multivariate time series, with optional de-meaning.

Usage

## S3 method for class 'matrix'
logret(dat, demean = FALSE, standardize = FALSE, ...)

## S3 method for class 'data.frame'
logret(dat, demean = FALSE, standardize = FALSE, ...)

Arguments

dat

The raw data, a matrix or data frame with n (number of timepoints) rows and m (number of component series) columns.

demean

Logical value indicating whether the data should be de-meaned.

standardize

Logical value indicating whether the data should be standardized (in the sense that each component series has an empirical variance equal to one).

...

Ignored.

Value

Matrix containing the log returns of the (de-meaned) data.

Plot log-variances over time.

Description

logvartimeplot plots the idiosyncratic and factor log-variances over time.

Usage

logvartimeplot(x, fsvsimobj = NULL, show = "both", maxrows = 5)

Arguments

x

Object of class 'fsvdraws', usually resulting from a call to fsvsample.

fsvsimobj

To indicate data generating values in case of simulated data, pass an object of type fsvsim (usually the result of a call to fsvsim).

show

If set to "fac", only factor log-volatilities will be displayed. If set to "idi", only idiosyncratic log-volatilities will be displayed. If set to "both", factor log-volatilities will be drawn first, followed by the idiosyncratic log-volatilities.

maxrows

Indicates the maximum number of rows to be drawn per page.

Details

This function displays the posterior distribution (mean +/- 2sd) of log-variances of both the factors and the idiosyncratic series. If these haven't been stored during sampling, logvartimeplot produces an error.

Value

Returns x invisibly.

A posteriori factor order identification

Description

orderident provides some (very ad-hoc) methods for identifying the ordering of the factors after running the (unrestricted) MCMC sampler by ordering according to the argument method.

Usage

orderident(x, method = "summed")

Arguments

x

Object of class 'fsvdraws', usually resulting from a call to fsvsample.

method

Methods currently supported:

summean Sort by sum of mean loadings (descending).
summeaninv Sort by sum of mean loadings (ascending).
summeanabs Sort by sum of mean absolute loadings (descending).
summed Sort by sum of median loadings (descending).
summedinv Sort by sum of median loadings (ascending).
summedabs Sort by sum of median absolute loadings (descending).
maxmed Sort by maximum median loadings (descending).
maxmedinv Sort by maximum median loadings (ascending).
maxmedrel Sort by maximum median loadings, relative to the sum of all median loadings on that factor (descending).
maxmedabsrel Sort by maximum absolute median loadings, relative to the sum of all median loadings on that factor (descending).

Value

Returns an object of class 'fsvdraws' with adjusted ordering.

Trace plots of parameter draws.

Description

paratraceplot draws trace plots of all parameters (mu, phi, sigma). Can be an important tool to check MCMC convergence if inference about (certain) parameters is sought.

Usage

## S3 method for class 'fsvdraws'
paratraceplot(x, fsvsimobj = NULL, thinning = NULL, maxrows = 3, ...)

Arguments

x

Object of class 'fsvdraws', usually resulting from a call to fsvsample.

fsvsimobj

To indicate data generating values in case of simulated data, pass an object of type fsvsim (usually the result of a call to fsvsim).

thinning

Plot every thinningth draw.

maxrows

Indicates the maximum number of rows to be drawn per page.

...

Ignored.

Value

Returns x invisibly.

Default factor SV plot

Description

Displays the correlation matrix at the last sampling point in time.

Usage

## S3 method for class 'fsvdraws'
plot(x, quantiles = c(0.05, 0.5, 0.95), col = NULL, fsvsimobj = NULL, ...)

Arguments

x

Object of class 'fsvdraws', usually resulting from a call to fsvsample.

quantiles

Posterior quantiles to be visualized. Must be of length 1 or 3.

col

Optional color palette.

fsvsimobj

To indicate data generating values in case of simulated data, pass an optional object of type fsvsim (usually the result of a call to fsvsim).

...

Other arguments will be passed on to corrplot.

Value

Returns x invisibly.

Several factor SV plots useful for model diagnostics

Description

Draws a collection of plots to explore the posterior distribution of a fitted factor SV model.

Usage

plotalot(x, fsvsimobj = NULL, ...)

Arguments

x

Object of class 'fsvdraws', usually resulting from a call to fsvsample.

fsvsimobj

To indicate data generating values in case of simulated data, pass an object of type fsvsim (usually the result of a call to fsvsim).

...

Other arguments will be passed on to the subfunctions.

Value

Returns x invisibly.

Predicts means and variances conditionally on the factors

Description

predcond simulates from the posterior predictive distribution of the data, conditionally on realized values of the factors. This has the advantage that the predictive density can be written as the product of the marginals but introduces sampling uncertainty that grows with the number of factors used.

Usage

predcond(x, ahead = 1, each = 1, ...)

Arguments

x

Object of class 'fsvdraws', usually resulting from a call to fsvsample.

ahead

Vector of timepoints, indicating how many steps to predict ahead.

each

Single integer (or coercible to such) indicating how often should be drawn from the posterior predictive distribution for each draw that has been stored during MCMC sampling.

...

Ignored.

Value

List of class fsvpredcond containing two elements:

means: Array containing the draws of the predictive means.
vols: Array containing the draws of the predictive volatilities (square root of variances).

Examples


set.seed(1)
sim <- fsvsim(n = 500, series = 4, factors = 1) # simulate
res <- fsvsample(sim$y, factors = 1) # estimate

# Predict 1 day ahead:
predobj <- predcond(res, each = 5)

# Draw from the predictive distribution:
preddraws <- matrix(rnorm(length(predobj$means[,,1]),
                    mean = predobj$means[,,1],
                    sd = predobj$vols[,,1]), nrow = 4)

# Visualize the predictive distribution
pairs(t(preddraws), col = rgb(0,0,0,.1), pch = 16)

Predicts correlation matrix

Description

predcor simulates from the posterior predictive distribution of the model-implied correlation matrix.

Usage

predcor(x, ahead = 1, each = 1)

Arguments

x

Object of class 'fsvdraws', usually resulting from a call to fsvsample.

ahead

Vector of timepoints, indicating how many steps to predict ahead.

each

Single integer (or coercible to such) indicating how often should be drawn from the posterior predictive distribution for each draw that has been stored during MCMC sampling.

Value

4-dimensional array containing draws from the predictive correlation distribution.

Note

Currently crudely implemented as a triple loop in pure R, may be slow.

Examples


set.seed(1)
sim <- fsvsim(series = 3, factors = 1) # simulate
res <- fsvsample(sim$y, factors = 1) # estimate

# Predict 1, 10, and 100 days ahead:
predobj <- predcor(res, ahead = c(1, 10, 100))

# Trace plot of draws from posterior predictive distribution
# of the correlation of Sim1 and Sim2:
# (one, ten, and 100 days ahead):
plot.ts(predobj[1,2,,])

# Smoothed kernel density estimates of predicted covariance
# of Sim1 and Sim2:
plot(density(predobj[1,2,,"1"], adjust = 2))
lines(density(predobj[1,2,,"10"], adjust = 2), col = 2)
lines(density(predobj[1,2,,"100"], adjust = 2), col = 3)

Predicts covariance matrix

Description

predcov simulates from the posterior predictive distribution of the model-implied covariance matrix.

Usage

predcov(x, ahead = 1, each = 1)

Arguments

x

Object of class 'fsvdraws', usually resulting from a call to fsvsample.

ahead

Vector of timepoints, indicating how many steps to predict ahead.

each

Single integer (or coercible to such) indicating how often should be drawn from the posterior predictive distribution for each draw that has been stored during MCMC sampling.

Value

4-dimensional array containing draws from the predictive covariance distribution.

Note

Currently crudely implemented as a triple loop in pure R, may be slow.

Examples


set.seed(1)
sim <- fsvsim(series = 3, factors = 1) # simulate
res <- fsvsample(sim$y, factors = 1) # estimate

# Predict 1, 10, and 100 days ahead:
predobj <- predcov(res, ahead = c(1, 10, 100))

# Trace plot of draws from posterior predictive distribution
# of the covariance of Sim1 and Sim2:
# (one, ten, and 100 days ahead):
plot.ts(predobj[1,2,,])

# Smoothed kernel density estimates of predicted covariance
# of Sim1 and Sim2:
plot(density(predobj[1,2,,"1"], adjust = 2))
lines(density(predobj[1,2,,"10"], adjust = 2), col = 2)
lines(density(predobj[1,2,,"100"], adjust = 2), col = 3)

Predicts factor and idiosyncratic log-volatilities h

Description

predh simulates from the posterior predictive distribution of the latent log-variances h, both for factors as well as for idiosyncratic series.

Usage

predh(x, ahead = 1, each = 1)

Arguments

x

Object of class 'fsvdraws', usually resulting from a call to fsvsample.

ahead

Vector of timepoints, indicating how many steps to predict ahead.

each

Single integer (or coercible to such) indicating how often should be drawn from the posterior predictive distribution for each draw that has been stored during MCMC sampling.

Value

List of class fsvpredh containing two elements:

idih: Array containing the draws of the latent idiosyncratic log-volatilities.
factorh: Array containing the draws of the latent factor log-volatilities.

Examples


set.seed(1)
sim <- fsvsim(series = 3, factors = 1) # simulate
res <- fsvsample(sim$y, factors = 1) # estimate

# Predict 1, 10, and 100 days ahead:
predobj <- predh(res, ahead = c(1, 10, 100))

# Trace plot of draws from posterior predictive factor log-variance
# (one, ten, and 100 days ahead):
plot.ts(predobj$factorh[1,,])

# Smoothed kernel density estimates of predicted volas:
plot(density(exp(predobj$factorh[1,,"1"]/2), adjust = 2))
lines(density(exp(predobj$factorh[1,,"10"]/2), adjust = 2), col = 2)
lines(density(exp(predobj$factorh[1,,"100"]/2), adjust = 2), col = 3)

Evaluates the predictive log likelihood using the predicted covariance matrix

Description

predloglik approximates the predictive log likelihood by simulating from the predictive distribution of the covariance matrix and evaluating the corresponding multivariate normal distribution.

Usage

predloglik(
  x,
  y,
  ahead = 1,
  each = 1,
  alldraws = FALSE,
  indicator = rep(TRUE, ncol(y))
)

Arguments

x

Object of class 'fsvdraws', usually resulting from a call to fsvsample.

y

Matrix of dimension length(ahead) times m where the predictive density should be evaluated.

ahead

Vector of timepoints, indicating how many steps to predict ahead.

each

Single integer (or coercible to such) indicating how often should be drawn from the posterior predictive distribution for each draw that has been stored during MCMC sampling.

alldraws

Should all the draws be returned or just the final results? (Can be useful to assess convergence.)

indicator

Logical vector of length m indicating which component series should be evaluated. The default is to evaluate all of them.

Value

Vector of length length(ahead) with log predictive likelihoods.

Examples


set.seed(1)

# Simulate a time series of length 1100:
sim <- fsvsim(n = 1100, series = 3, factors = 1)
y <- sim$y

# Estimate using only 1000 days:
res <- fsvsample(y[seq_len(1000),], factors = 1)

# Evaluate the 1, 10, and 100 days ahead predictive log
# likelihood:
ahead <- c(1, 10, 100)
scores <- predloglik(res, y[1000+ahead,], ahead = ahead, each = 10)
print(scores)

Evaluates the predictive log likelihood using the Woodbury identity

Description

predloglikWB approximates the predictive log likelihood exploiting the factor structure and using the Woodbury idenitity and the corresponding matrix determinant lemma. This is recommended only if many series and few factors are present.

Usage

predloglikWB(x, y, ahead = 1, each = 1, alldraws = FALSE)

Arguments

x

Object of class 'fsvdraws', usually resulting from a call to fsvsample.

y

Matrix of dimension length(ahead) times m where the predictive density should be evaluated.

ahead

Vector of timepoints, indicating how many steps to predict ahead.

each

Single integer (or coercible to such) indicating how often should be drawn from the posterior predictive distribution for each draw that has been stored during MCMC sampling.

alldraws

Should all the draws be returned or just the final results? (Can be useful to assess convergence.)

Value

Vector of length length(ahead) with log predictive likelihoods.

Note

Currently crudely implemented as a triple loop in pure R, may be slow.

Examples


set.seed(1)

# Simulate a time series of length 1100:
sim <- fsvsim(n = 1100, series = 3, factors = 1)
y <- sim$y

# Estimate using only 1000 days:
res <- fsvsample(y[seq_len(1000),], factors = 1)

# Evaluate the 1, 10, and 100 days ahead predictive log
# likelihood:
ahead <- c(1, 10, 100)
scores <- predloglikWB(res, y[1000+ahead,], ahead = ahead, each = 10)
print(scores)

Predicts precision matrix and its determinant (Woodbury variant)

Description

predprecWB simulates from the posterior predictive distribution of the model-implied precision matrix and its determinant using the Woodbury matrix identity and the matrix determinant lemma

Usage

predprecWB(x, ahead = 1, each = 1)

Arguments

x

Object of class 'fsvdraws', usually resulting from a call to fsvsample.

ahead

Vector of timepoints, indicating how many steps to predict ahead.

each

Single integer (or coercible to such) indicating how often should be drawn from the posterior predictive distribution for each draw that has been stored during MCMC sampling.

Value

List containing two elements:

precision: Array containing the draws of the predicted precision matrix.
precisionlogdet: Matrix containing the draws of the determinant of the predicted precision matrix.

Note

Currently crudely implemented as a triple loop in pure R, may be slow.

Ad-hoc methods for determining the order of variables

Description

In factor SV models, the ordering of variables is often chosen through a preliminary static factor analysis. These methods are implemented in preorder. After a maximum likelihood factor model fit to the data, factor loadings are ordered as follows: The variable with the highest loading on factor 1 is placed first, the variable with the highest loading on factor 2 second (unless this variable is already placed first, in which case the variable with the second highest loading is taken).

Usage

preorder(
  dat,
  factors = ledermann(ncol(dat)),
  type = "fixed",
  transload = identity
)

Arguments

dat

Matrix containing the data, with n rows (points in time) and m columns (component series).

factors

Number of factors to be used, defaults to the Ledermann bound.

type

Can be "fixed" or "dynamic". The option "fixed" means that that a factors-factor model is fit once and the entire ordering is determined according to this fit (the default). The option "dynamic" means that the model is re-fit factors times with the number of factors going from 1 to factors and in each round the correspondingly largest loading is chosen.

transload

Function for transforming the estimated factor loadings before ordering. Defaults to the identity function.

Value

A vector of length m with the ordering found.

Pretty printing of an fsvsdraws object

Description

Pretty printing of an fsvsdraws object

Usage

## S3 method for class 'fsvdraws'
print(x, ...)

Arguments

x

Object of class 'fsvdraws', usually resulting from a call of fsvsample.

...

Ignored.

Value

Returns x invisibly.

Extract summary statistics for the posterior correlation matrix which have been stored during sampling

Description

runningcormat extracts summary statistics from the model-implied correlation matrix from an fsvdraws object for one point in time.

Usage

runningcormat(x, i, statistic = "mean", type = "cor")

Arguments

x

Object of class 'fsvdraws', usually resulting from a call of fsvsample.

i

A single point in time.

statistic

Indicates which statistic should be extracted. Defaults to 'mean'.

type

Indicates whether covariance (cov) or correlation (cor) should be extracted.

Value

Matrix containing the requested correlation matrix summary statistic.

Examples


set.seed(1)
sim <- fsvsim(n = 500, series = 3, factors = 1) # simulate
res <- fsvsample(sim$y, factors = 1, runningstore = 6) # estimate

cor100mean <- runningcormat(res, 100) # extract mean at t = 100
cor100sd <- runningcormat(res, 100, statistic = "sd") # extract sd
lower <- cor100mean - 2*cor100sd
upper <- cor100mean + 2*cor100sd

true <- cormat(sim, 100)[,,1] # true value

# Visualize mean +/- 2sd and data generating values
par(mfrow = c(3,3), mar = c(2, 2, 2, 2))
for (i in 1:3) {
 for (j in 1:3) {
  plot(cor100mean[i,j], ylim = range(lower, upper), pch = 3,
  main = paste(i, j, sep = ' vs. '), xlab = '', ylab = '')
  lines(c(1,1), c(lower[i,j], upper[i,j]))
  points(true[i,j], col = 3, cex = 2)
 }
}

Extract summary statistics for the posterior covariance matrix which have been stored during sampling

Description

runningcovmat extracts summary statistics from the model-implied covariance matrix from an fsvdraws object for one point in time.

Usage

runningcovmat(x, i, statistic = "mean", type = "cov")

Arguments

x

Object of class 'fsvdraws', usually resulting from a call of fsvsample.

i

A single point in time.

statistic

Indicates which statistic should be extracted. Defaults to 'mean'.

type

Indicates whether covariance (cov) or correlation (cor) should be extracted.

Value

Matrix containing the requested covariance matrix summary statistic.

Examples


set.seed(1)
sim <- fsvsim(n = 500, series = 3, factors = 1) # simulate
res <- fsvsample(sim$y, factors = 1) # estimate

cov100mean <- runningcovmat(res, 100) # extract mean at t = 100
cov100sd <- runningcovmat(res, 100, statistic = "sd") # extract sd
lower <- cov100mean - 2*cov100sd
upper <- cov100mean + 2*cov100sd

true <- covmat(sim, 100) # true value

# Visualize mean +/- 2sd and data generating values
par(mfrow = c(3,3), mar = c(2, 2, 2, 2))
for (i in 1:3) {
 for (j in 1:3) {
  plot(cov100mean[i,j], ylim = range(lower, upper), pch = 3,
  main = paste(i, j, sep = ' vs. '), xlab = '', ylab = '')
  lines(c(1,1), c(lower[i,j], upper[i,j]))
  points(true[i,j,1], col = 3, cex = 2)
 }
}

A posteriori sign identification

Description

signident provides methods for identifying the signs of the factor loadings after running the MCMC sampler

Usage

signident(x, method = "maximin", implementation = 3)

Arguments

x

Object of class 'fsvdraws', usually resulting from a call to fsvsample.

method

Can be "diagonal" or "maximin". If "diagonal" is chosen, the diagonal elements of the factor loadings matrix are assumed to have positive signs and the others are arranged accordingly. If "maximin" is chosen, for each factor, signident looks for the series where the minimum absolute loadings are biggest and chooses this series to have positive loadings.

implementation

Either 1, 2, or 3 (the default). Determines how the reordering is implemented. Should not be necessary to depart from the default.

Value

Returns an object of class 'fsvdraws' with adjusted factors and factor loadings. Moreover, a list element called 'identifier' is added, providing the numbers of the series used for identification and the corresponding minimum distances to zero.

Examples


set.seed(1)
sim <- fsvsim(series = 8, factors = 2) # simulate
res <- fsvsample(sim$y, factors = 2, signswitch = TRUE,
                 draws = 2000, burnin = 1000) # estimate

# Plot unidentified loadings:
facloaddensplot(res, fsvsimobj = sim, rows = 8)

# Identify:
res <- signident(res)

# Plot identified loadings:
facloaddensplot(res, fsvsimobj = sim, rows = 8)

Plot series-specific volatilities over time.

Description

voltimeplot plots the marginal volatilities over time, i.e. the series-specific conditional standard deviations. If these haven't been stored during sampling (because runningstore has been set too low), voltimeplot throws a warning.

Usage

voltimeplot(x, these = seq_len(nrow(x$y)), legend = "topright", ...)

Arguments

x

Object of class 'fsvdraws', usually resulting from a call to fsvsample.

these

Index vector containing the time points to plot. Defaults to seq_len(nrow(x$y)), i.e., all timepoints.

legend

Where to position the legend. If set to NULL, labels will be put directly next to the series. Defaults to "topright".

...

Additional parameters will be passed on to ts.plot.

Value

Returns x invisibly.

Bayesian Estimation of (Sparse) Latent Factor Stochastic Volatility Models through MCMC

Description

Details

Note

Author(s)

References

See Also

Examples

Plot communalities over time.

Description

Usage

Arguments

Details

Value

See Also

Extract "true" model-implied correlations of two series only

Description

Usage

Arguments

Value

See Also

Plot correlation matrices for certain points in time

Description

Usage

Arguments

Value

Note

See Also

Generic extraction of correlation matrix

Description

Usage

Arguments

Value

See Also

Extract posterior draws of the model-implied correlation matrix

Description

Usage

Arguments

Value

Note

See Also

Examples

Extract "true" model-implied correlation matrix for several points in time

Description

Usage

Arguments

Value

Note

See Also

Plots pairwise correlations over time

Description

Usage

Arguments

Value

See Also

Plot correlations over time.

Description

Usage

Arguments

Details

Value

See Also

Extract "true" model-implied covariances of two series only

Description

Usage

Arguments

Value

See Also

Generic extraction of covariance matrix

Description

Usage

Arguments

Value

See Also

Extract posterior draws of the model-implied covariance matrix

Description

Usage

Arguments

Value

Note