Type:	Package
Title:	Bayesian Vector Heterogeneous Autoregressive Modeling
Version:	2.3.0
Description:	Tools to model and forecast multivariate time series including Bayesian Vector heterogeneous autoregressive (VHAR) model by Kim & Baek (2023) (<doi:10.1080/00949655.2023.2281644>). 'bvhar' can model Vector Autoregressive (VAR), VHAR, Bayesian VAR (BVAR), and Bayesian VHAR (BVHAR) models.
License:	GPL (≥ 3)
URL:	https://ygeunkim.github.io/package/bvhar/, https://github.com/ygeunkim/bvhar
BugReports:	https://github.com/ygeunkim/bvhar/issues
Suggests:	covr, knitr, parallel, rmarkdown, testthat (≥ 3.0.0)
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.2
Imports:	lifecycle, Rcpp, ggplot2, tidyr, tibble, dplyr, foreach, purrr, stats, optimParallel, posterior, bayesplot, utils
LinkingTo:	BH (≥ 1.87.0-0), Rcpp (≥ 0.10.0), RcppEigen (≥ 0.3.4.0.0), RcppSpdlog, RcppThread
VignetteBuilder:	knitr
Depends:	R (≥ 4.2.0)
Config/testthat/edition:	3
NeedsCompilation:	yes
Packaged:	2025-06-25 05:54:35 UTC; ygmini
Author:	Young Geun Kim [aut, cre, cph], Changryong Baek [ctb]
Maintainer:	Young Geun Kim <ygeunkimstat@gmail.com>
Repository:	CRAN
Date/Publication:	2025-06-25 06:20:02 UTC

bvhar: Bayesian Vector Heterogeneous Autoregressive Modeling

Description

Tools to model and forecast multivariate time series including Bayesian Vector heterogeneous autoregressive (VHAR) model by Kim & Baek (2023) (doi:10.1080/00949655.2023.2281644). 'bvhar' can model Vector Autoregressive (VAR), VHAR, Bayesian VAR (BVAR), and Bayesian VHAR (BVHAR) models.

Details

The bvhar package provides function to analyze and forecast multivariate time series data via vector autoregressive modeling. Here, vector autoregressive modelling includes:

• Vector autoregressive (VAR) model: var_lm()

• Vector heterogeneous autoregressive (VHAR) model: vhar_lm()

• Bayesian VAR (BVAR) model: var_bayes()

• Bayesian VHAR (BVHAR) model: vhar_bayes()

Author(s)

Maintainer: Young Geun Kim ygeunkimstat@gmail.com (ORCID) [copyright holder]

Other contributors:

• Changryong Baek [contributor]

References

Kim, Y. G., and Baek, C. (2024). Bayesian vector heterogeneous autoregressive modeling. Journal of Statistical Computation and Simulation, 94(6), 1139-1157.

Kim, Y. G., and Baek, C. (n.d.). Working paper.

See Also

Useful links:

• https://ygeunkim.github.io/package/bvhar/

• https://github.com/ygeunkim/bvhar

• Report bugs at https://github.com/ygeunkim/bvhar/issues

Final Prediction Error Criterion

Description

Compute FPE of VAR(p) and VHAR

Usage

FPE(object, ...)

## S3 method for class 'varlse'
FPE(object, ...)

## S3 method for class 'vharlse'
FPE(object, ...)

Arguments

Details

Let \tilde{\Sigma}_e be the MLE and let \hat{\Sigma}_e be the unbiased estimator (covmat) for \Sigma_e. Note that

\tilde{\Sigma}_e = \frac{n - k}{T} \hat{\Sigma}_e

Then

FPE(p) = (\frac{n + k}{n - k})^m \det \tilde{\Sigma}_e

Value

FPE value.

References

Lütkepohl, H. (2007). New Introduction to Multiple Time Series Analysis. Springer Publishing.

Hannan-Quinn Criterion

Description

Compute HQ of VAR(p), VHAR, BVAR(p), and BVHAR

Usage

HQ(object, ...)

## S3 method for class 'logLik'
HQ(object, ...)

## S3 method for class 'varlse'
HQ(object, ...)

## S3 method for class 'vharlse'
HQ(object, ...)

## S3 method for class 'bvarmn'
HQ(object, ...)

## S3 method for class 'bvarflat'
HQ(object, ...)

## S3 method for class 'bvharmn'
HQ(object, ...)

Arguments

Details

The formula is

HQ = -2 \log p(y \mid \hat\theta) + k \log\log(T)

which can be computed by AIC(object, ..., k = 2 * log(log(nobs(object)))) with stats::AIC().

Let \tilde{\Sigma}_e be the MLE and let \hat{\Sigma}_e be the unbiased estimator (covmat) for \Sigma_e. Note that

\tilde{\Sigma}_e = \frac{n - k}{T} \hat{\Sigma}_e

Then

HQ(p) = \log \det \Sigma_e + \frac{2 \log \log n}{n}(\text{number of freely estimated parameters})

where the number of freely estimated parameters is pm^2.

Value

HQ value.

References

Hannan, E.J. and Quinn, B.G. (1979). The Determination of the Order of an Autoregression. Journal of the Royal Statistical Society: Series B (Methodological), 41: 190-195.

Hannan, E.J. and Quinn, B.G. (1979). The Determination of the Order of an Autoregression. Journal of the Royal Statistical Society: Series B (Methodological), 41: 190-195.

Lütkepohl, H. (2007). New Introduction to Multiple Time Series Analysis. Springer Publishing.

Quinn, B.G. (1980). Order Determination for a Multivariate Autoregression. Journal of the Royal Statistical Society: Series B (Methodological), 42: 182-185.

Convert VAR to VMA(infinite)

Description

Convert VAR process to infinite vector MA process

Usage

VARtoVMA(object, lag_max)

Arguments

Details

Let VAR(p) be stable.

Y_t = c + \sum_{j = 0} W_j Z_{t - j}

For VAR coefficient B_1, B_2, \ldots, B_p,

I = (W_0 + W_1 L + W_2 L^2 + \cdots + ) (I - B_1 L - B_2 L^2 - \cdots - B_p L^p)

Recursively,

W_0 = I

W_1 = W_0 B_1 (W_1^T = B_1^T W_0^T)

W_2 = W_1 B_1 + W_0 B_2 (W_2^T = B_1^T W_1^T + B_2^T W_0^T)

W_j = \sum_{j = 1}^k W_{k - j} B_j (W_j^T = \sum_{j = 1}^k B_j^T W_{k - j}^T)

Value

VMA coefficient of k(lag-max + 1) x k dimension

References

Lütkepohl, H. (2007). New Introduction to Multiple Time Series Analysis. Springer Publishing.

Convert VHAR to VMA(infinite)

Description

Convert VHAR process to infinite vector MA process

Usage

VHARtoVMA(object, lag_max)

Arguments

Details

Let VAR(p) be stable and let VAR(p) be Y_0 = X_0 B + Z

VHAR is VAR(22) with

Y_0 = X_1 B + Z = ((X_0 \tilde{T}^T)) \Phi + Z

Observe that

B = \tilde{T}^T \Phi

Value

VMA coefficient of k(lag-max + 1) x k dimension

References

Lütkepohl, H. (2007). New Introduction to Multiple Time Series Analysis. Springer Publishing.

Evaluate the Density Forecast Based on Average Log Predictive Likelihood (APLP)

Description

This function computes ALPL given forecasting of Bayesian models.

Usage

alpl(x, ...)

## S3 method for class 'bvharcv'
alpl(x, ...)

Arguments

Dynamic Spillover Indices Plot

Description

Draws dynamic directional spillover plot.

Usage

## S3 method for class 'bvhardynsp'
autoplot(
  object,
  type = c("tot", "to", "from", "net"),
  hcol = "grey",
  hsize = 1.5,
  row_facet = NULL,
  col_facet = NULL,
  ...
)

Arguments

Plot Impulse Responses

Description

Draw impulse responses of response ~ impulse in the facet.

Usage

## S3 method for class 'bvharirf'
autoplot(object, ...)

Arguments

Value

A ggplot object

See Also

irf()

Plot the Result of BVAR and BVHAR MCMC

Description

Draw BVAR and BVHAR MCMC plots.

Usage

## S3 method for class 'bvharsp'
autoplot(
  object,
  type = c("coef", "trace", "dens", "area"),
  pars = character(),
  regex_pars = character(),
  ...
)

Arguments

Value

A ggplot object

Residual Plot for Minnesota Prior VAR Model

Description

This function draws residual plot for covariance matrix of Minnesota prior VAR model.

Usage

## S3 method for class 'normaliw'
autoplot(object, hcol = "grey", hsize = 1.5, ...)

Arguments

Value

A ggplot object

Plot Forecast Result

Description

Plots the forecasting result with forecast regions.

Usage

## S3 method for class 'predbvhar'
autoplot(
  object,
  type = c("grid", "wrap"),
  ci_alpha = 0.7,
  alpha_scale = 0.3,
  x_cut = 1,
  viridis = FALSE,
  viridis_option = "D",
  NROW = NULL,
  NCOL = NULL,
  ...
)

## S3 method for class 'predbvhar'
autolayer(object, ci_fill = "grey70", ci_alpha = 0.5, alpha_scale = 0.3, ...)

Arguments

Value

A ggplot object

A ggplot layer

Plot the Heatmap of SSVS Coefficients

Description

Draw heatmap for SSVS prior coefficients.

Usage

## S3 method for class 'summary.bvharsp'
autoplot(object, point = FALSE, ...)

Arguments

Value

A ggplot object

Density Plot for Minnesota Prior VAR Model

Description

This function draws density plot for coefficient matrices of Minnesota prior VAR model.

Usage

## S3 method for class 'summary.normaliw'
autoplot(
  object,
  type = c("trace", "dens", "area"),
  pars = character(),
  regex_pars = character(),
  ...
)

Arguments

Value

A ggplot object

Setting Empirical Bayes Optimization Bounds

Description

This function sets lower and upper bounds for set_bvar(), set_bvhar(), or set_weight_bvhar().

Usage

bound_bvhar(
  init_spec = set_bvhar(),
  lower_spec = set_bvhar(),
  upper_spec = set_bvhar()
)

## S3 method for class 'boundbvharemp'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

is.boundbvharemp(x)

## S3 method for class 'boundbvharemp'
knit_print(x, ...)

Arguments

Value

boundbvharemp class

Fitting Bayesian VAR(p) of Flat Prior

Description

This function fits BVAR(p) with flat prior.

Usage

bvar_flat(
  y,
  p,
  num_chains = 1,
  num_iter = 1000,
  num_burn = floor(num_iter/2),
  thinning = 1,
  bayes_spec = set_bvar_flat(),
  include_mean = TRUE,
  verbose = FALSE,
  num_thread = 1
)

## S3 method for class 'bvarflat'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

## S3 method for class 'bvarflat'
logLik(object, ...)

## S3 method for class 'bvarflat'
AIC(object, ...)

## S3 method for class 'bvarflat'
BIC(object, ...)

is.bvarflat(x)

## S3 method for class 'bvarflat'
knit_print(x, ...)

Arguments

Details

Ghosh et al. (2018) gives flat prior for residual matrix in BVAR.

Under this setting, there are many models such as hierarchical or non-hierarchical. This function chooses the most simple non-hierarchical matrix normal prior in Section 3.1.

A \mid \Sigma_e \sim MN(0, U^{-1}, \Sigma_e)

where U: precision matrix (MN: matrix normal).

p (\Sigma_e) \propto 1

Value

bvar_flat() returns an object bvarflat class. It is a list with the following components:

coefficients

Posterior Mean matrix of Matrix Normal distribution

fitted.values

Fitted values

residuals

Residuals

mn_prec

Posterior precision matrix of Matrix Normal distribution

iw_scale

Posterior scale matrix of posterior inverse-wishart distribution

iw_shape

Posterior shape of inverse-wishart distribution

df

Numer of Coefficients: mp + 1 or mp

p

Lag of VAR

m

Dimension of the time series

obs

Sample size used when training = totobs - p

totobs

Total number of the observation

process

Process string in the bayes_spec: BVAR_Flat

spec

Model specification (bvharspec)

type

include constant term (const) or not (none)

call

Matched call

prior_mean

Prior mean matrix of Matrix Normal distribution: zero matrix

prior_precision

Prior precision matrix of Matrix Normal distribution: U^{-1}

y0

Y_0

design

X_0

y

Raw input (matrix)

References

Ghosh, S., Khare, K., & Michailidis, G. (2018). High-Dimensional Posterior Consistency in Bayesian Vector Autoregressive Models. Journal of the American Statistical Association, 114(526).

Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions: Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25.

See Also

• set_bvar_flat() to specify the hyperparameters of BVAR flat prior.

• coef.bvarflat(), residuals.bvarflat(), and fitted.bvarflat()

• predict.bvarflat() to forecast the BVHAR process

Fitting Bayesian VAR(p) of Minnesota Prior

Description

This function fits BVAR(p) with Minnesota prior.

Usage

bvar_minnesota(
  y,
  p = 1,
  num_chains = 1,
  num_iter = 1000,
  num_burn = floor(num_iter/2),
  thinning = 1,
  bayes_spec = set_bvar(),
  scale_variance = 0.05,
  include_mean = TRUE,
  parallel = list(),
  verbose = FALSE,
  num_thread = 1
)

## S3 method for class 'bvarmn'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

## S3 method for class 'bvarhm'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

## S3 method for class 'bvarmn'
logLik(object, ...)

## S3 method for class 'bvarmn'
AIC(object, ...)

## S3 method for class 'bvarmn'
BIC(object, ...)

is.bvarmn(x)

## S3 method for class 'bvarmn'
knit_print(x, ...)

## S3 method for class 'bvarhm'
knit_print(x, ...)

Arguments

Details

Minnesota prior gives prior to parameters A (VAR matrices) and \Sigma_e (residual covariance).

A \mid \Sigma_e \sim MN(A_0, \Omega_0, \Sigma_e)

\Sigma_e \sim IW(S_0, \alpha_0)

(MN: matrix normal, IW: inverse-wishart)

Value

bvar_minnesota() returns an object bvarmn class. It is a list with the following components:

coefficients

Posterior Mean

fitted.values

Fitted values

residuals

Residuals

mn_mean

Posterior mean matrix of Matrix Normal distribution

mn_prec

Posterior precision matrix of Matrix Normal distribution

iw_scale

Posterior scale matrix of posterior inverse-Wishart distribution

iw_shape

Posterior shape of inverse-Wishart distribution (alpha_0 - obs + 2). \alpha_0: nrow(Dummy observation) - k

df

Numer of Coefficients: mp + 1 or mp

m

Dimension of the time series

obs

Sample size used when training = totobs - p

prior_mean

Prior mean matrix of Matrix Normal distribution: A_0

prior_precision

Prior precision matrix of Matrix Normal distribution: \Omega_0^{-1}

prior_scale

Prior scale matrix of inverse-Wishart distribution: S_0

prior_shape

Prior shape of inverse-Wishart distribution: \alpha_0

y0

Y_0

design

X_0

p

Lag of VAR

totobs

Total number of the observation

type

include constant term (const) or not (none)

y

Raw input (matrix)

call

Matched call

process

Process string in the bayes_spec: BVAR_Minnesota

spec

Model specification (bvharspec)

It is also normaliw and bvharmod class.

References

Bańbura, M., Giannone, D., & Reichlin, L. (2010). Large Bayesian vector auto regressions. Journal of Applied Econometrics, 25(1).

Giannone, D., Lenza, M., & Primiceri, G. E. (2015). Prior Selection for Vector Autoregressions. Review of Economics and Statistics, 97(2).

Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions: Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25.

KADIYALA, K.R. and KARLSSON, S. (1997), NUMERICAL METHODS FOR ESTIMATION AND INFERENCE IN BAYESIAN VAR-MODELS. J. Appl. Econ., 12: 99-132.

Karlsson, S. (2013). Chapter 15 Forecasting with Bayesian Vector Autoregression. Handbook of Economic Forecasting, 2, 791-897.

Sims, C. A., & Zha, T. (1998). Bayesian Methods for Dynamic Multivariate Models. International Economic Review, 39(4), 949-968.

See Also

• set_bvar() to specify the hyperparameters of Minnesota prior.

• summary.normaliw() to summarize BVAR model

Examples

# Perform the function using etf_vix dataset
fit <- bvar_minnesota(y = etf_vix[,1:3], p = 2)
class(fit)

# Extract coef, fitted values, and residuals
coef(fit)
head(residuals(fit))
head(fitted(fit))

Fitting Bayesian VHAR of Minnesota Prior

Description

This function fits BVHAR with Minnesota prior.

Usage

bvhar_minnesota(
  y,
  har = c(5, 22),
  num_chains = 1,
  num_iter = 1000,
  num_burn = floor(num_iter/2),
  thinning = 1,
  bayes_spec = set_bvhar(),
  scale_variance = 0.05,
  include_mean = TRUE,
  parallel = list(),
  verbose = FALSE,
  num_thread = 1
)

## S3 method for class 'bvharmn'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

## S3 method for class 'bvharhm'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

## S3 method for class 'bvharmn'
logLik(object, ...)

## S3 method for class 'bvharmn'
AIC(object, ...)

## S3 method for class 'bvharmn'
BIC(object, ...)

is.bvharmn(x)

## S3 method for class 'bvharmn'
knit_print(x, ...)

## S3 method for class 'bvharhm'
knit_print(x, ...)

Arguments

Details

Apply Minnesota prior to Vector HAR: \Phi (VHAR matrices) and \Sigma_e (residual covariance).

\Phi \mid \Sigma_e \sim MN(M_0, \Omega_0, \Sigma_e)

\Sigma_e \sim IW(\Psi_0, \nu_0)

(MN: matrix normal, IW: inverse-wishart)

There are two types of Minnesota priors for BVHAR:

• VAR-type Minnesota prior specified by set_bvhar(), so-called BVHAR-S model.

• VHAR-type Minnesota prior specified by set_weight_bvhar(), so-called BVHAR-L model.

Value

bvhar_minnesota() returns an object bvharmn class. It is a list with the following components:

coefficients

Posterior Mean

fitted.values

Fitted values

residuals

Residuals

mn_mean

Posterior mean matrix of Matrix Normal distribution

mn_prec

Posterior precision matrix of Matrix Normal distribution

iw_scale

Posterior scale matrix of posterior inverse-wishart distribution

iw_shape

Posterior shape of inverse-Wishart distribution (\nu_0 - obs + 2). \nu_0: nrow(Dummy observation) - k

df

Numer of Coefficients: 3m + 1 or 3m

m

Dimension of the time series

obs

Sample size used when training = totobs - 22

prior_mean

Prior mean matrix of Matrix Normal distribution: M_0

prior_precision

Prior precision matrix of Matrix Normal distribution: \Omega_0^{-1}

prior_scale

Prior scale matrix of inverse-Wishart distribution: \Psi_0

prior_shape

Prior shape of inverse-Wishart distribution: \nu_0

y0

Y_0

design

X_0

p

3, this element exists to run the other functions

week

Order for weekly term

month

Order for monthly term

totobs

Total number of the observation

type

include constant term (const) or not (none)

HARtrans

VHAR linear transformation matrix: C_{HAR}

y

Raw input (matrix)

call

Matched call

process

Process string in the bayes_spec: BVHAR_MN_VAR (BVHAR-S) or BVHAR_MN_VHAR (BVHAR-L)

spec

Model specification (bvharspec)

It is also normaliw and bvharmod class.

References

Kim, Y. G., and Baek, C. (2024). Bayesian vector heterogeneous autoregressive modeling. Journal of Statistical Computation and Simulation, 94(6), 1139-1157.

See Also

• set_bvhar() to specify the hyperparameters of BVHAR-S

• set_weight_bvhar() to specify the hyperparameters of BVHAR-L

• summary.normaliw() to summarize BVHAR model

Examples

# Perform the function using etf_vix dataset
fit <- bvhar_minnesota(y = etf_vix[,1:3])
class(fit)

# Extract coef, fitted values, and residuals
coef(fit)
head(residuals(fit))
head(fitted(fit))

Finding the Set of Hyperparameters of Bayesian Model

Description

This function chooses the set of hyperparameters of Bayesian model using stats::optim() function.

Usage

choose_bayes(
  bayes_bound = bound_bvhar(),
  ...,
  eps = 1e-04,
  y,
  order = c(5, 22),
  include_mean = TRUE,
  parallel = list()
)

Arguments

Value

bvharemp class is a list that has

...

Many components of stats::optim() or optimParallel::optimParallel()

spec

Corresponding bvharspec

fit

Chosen Bayesian model

ml

Marginal likelihood of the final model

References

Giannone, D., Lenza, M., & Primiceri, G. E. (2015). Prior Selection for Vector Autoregressions. Review of Economics and Statistics, 97(2).

Kim, Y. G., and Baek, C. (2024). Bayesian vector heterogeneous autoregressive modeling. Journal of Statistical Computation and Simulation, 94(6), 1139-1157.

See Also

• bound_bvhar() to define L-BFGS-B optimization bounds.

• Individual functions: choose_bvar()

Finding the Set of Hyperparameters of Individual Bayesian Model

Description

Instead of these functions, you can use choose_bayes().

Usage

choose_bvar(
  bayes_spec = set_bvar(),
  lower = 0.01,
  upper = 10,
  ...,
  eps = 1e-04,
  y,
  p,
  include_mean = TRUE,
  parallel = list()
)

choose_bvhar(
  bayes_spec = set_bvhar(),
  lower = 0.01,
  upper = 10,
  ...,
  eps = 1e-04,
  y,
  har = c(5, 22),
  include_mean = TRUE,
  parallel = list()
)

## S3 method for class 'bvharemp'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

is.bvharemp(x)

## S3 method for class 'bvharemp'
knit_print(x, ...)

Arguments

Details

Empirical Bayes method maximizes marginal likelihood and selects the set of hyperparameters. These functions implement L-BFGS-B method of stats::optim() to find the maximum of marginal likelihood.

If you want to set lower and upper option more carefully, deal with them like as in stats::optim() in order of set_bvar(), set_bvhar(), or set_weight_bvhar()'s argument (except eps). In other words, just arrange them in a vector.

Value

bvharemp class is a list that has

• stats::optim() or optimParallel::optimParallel()

• chosen bvharspec set

• Bayesian model fit result with chosen specification

  ...

  Many components of stats::optim() or optimParallel::optimParallel()

  spec

  Corresponding bvharspec

  fit

  Chosen Bayesian model

  ml

  Marginal likelihood of the final model

References

Byrd, R. H., Lu, P., Nocedal, J., & Zhu, C. (1995). A limited memory algorithm for bound constrained optimization. SIAM Journal on scientific computing, 16(5), 1190-1208.

Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2013). Bayesian data analysis. Chapman and Hall/CRC.

Giannone, D., Lenza, M., & Primiceri, G. E. (2015). Prior Selection for Vector Autoregressions. Review of Economics and Statistics, 97(2).

Kim, Y. G., and Baek, C. (2024). Bayesian vector heterogeneous autoregressive modeling. Journal of Statistical Computation and Simulation, 94(6), 1139-1157.

Choose the Best VAR based on Information Criteria

Description

This function computes AIC, FPE, BIC, and HQ up to p = lag_max of VAR model.

Usage

choose_var(y, lag_max = 5, include_mean = TRUE, parallel = FALSE)

Arguments

Value

Minimum order and information criteria values

Coefficient Matrix of Multivariate Time Series Models

Description

By defining stats::coef() for each model, this function returns coefficient matrix estimates.

Usage

## S3 method for class 'varlse'
coef(object, ...)

## S3 method for class 'vharlse'
coef(object, ...)

## S3 method for class 'bvarmn'
coef(object, ...)

## S3 method for class 'bvarflat'
coef(object, ...)

## S3 method for class 'bvharmn'
coef(object, ...)

## S3 method for class 'bvharsp'
coef(object, ...)

## S3 method for class 'summary.bvharsp'
coef(object, ...)

Arguments

Value

matrix object with appropriate dimension.

Deviance Information Criterion of Multivariate Time Series Model

Description

Compute DIC of BVAR and BVHAR.

Usage

compute_dic(object, ...)

## S3 method for class 'bvarmn'
compute_dic(object, n_iter = 100L, ...)

Arguments

Details

Deviance information criteria (DIC) is

- 2 \log p(y \mid \hat\theta_{bayes}) + 2 p_{DIC}

where p_{DIC} is the effective number of parameters defined by

p_{DIC} = 2 ( \log p(y \mid \hat\theta_{bayes}) - E_{post} \log p(y \mid \theta) )

Random sampling from posterior distribution gives its computation, \theta_i \sim \theta \mid y, i = 1, \ldots, M

p_{DIC}^{computed} = 2 ( \log p(y \mid \hat\theta_{bayes}) - \frac{1}{M} \sum_i \log p(y \mid \theta_i) )

Value

DIC value.

References

Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2013). Bayesian data analysis. Chapman and Hall/CRC.

Spiegelhalter, D.J., Best, N.G., Carlin, B.P. and Van Der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64: 583-639.

Extracting Log of Marginal Likelihood

Description

Compute log of marginal likelihood of Bayesian Fit

Usage

compute_logml(object, ...)

## S3 method for class 'bvarmn'
compute_logml(object, ...)

## S3 method for class 'bvharmn'
compute_logml(object, ...)

Arguments

Details

Closed form of Marginal Likelihood of BVAR can be derived by

p(Y_0) = \pi^{-mn / 2} \frac{\Gamma_m ((\alpha_0 + n) / 2)}{\Gamma_m (\alpha_0 / 2)} \det(\Omega_0)^{-m / 2} \det(S_0)^{\alpha_0 / 2} \det(\hat{V})^{- m / 2} \det(\hat{\Sigma}_e)^{-(\alpha_0 + n) / 2}

Closed form of Marginal Likelihood of BVHAR can be derived by

p(Y_0) = \pi^{-ms_0 / 2} \frac{\Gamma_m ((d_0 + n) / 2)}{\Gamma_m (d_0 / 2)} \det(P_0)^{-m / 2} \det(U_0)^{d_0 / 2} \det(\hat{V}_{HAR})^{- m / 2} \det(\hat{\Sigma}_e)^{-(d_0 + n) / 2}

Value

log likelihood of Minnesota prior model.

References

Giannone, D., Lenza, M., & Primiceri, G. E. (2015). Prior Selection for Vector Autoregressions. Review of Economics and Statistics, 97(2).

Evaluate the Sparsity Estimation Based on FDR

Description

This function computes false discovery rate (FDR) for sparse element of the true coefficients given threshold.

Usage

conf_fdr(x, y, ...)

## S3 method for class 'summary.bvharsp'
conf_fdr(x, y, truth_thr = 0, ...)

Arguments

Details

When using this function, the true coefficient matrix \Phi should be sparse. False discovery rate (FDR) is computed by

FDR = \frac{FP}{TP + FP}

where TP is true positive, and FP is false positive.

Value

FDR value in confusion table

References

Bai, R., & Ghosh, M. (2018). High-dimensional multivariate posterior consistency under global-local shrinkage priors. Journal of Multivariate Analysis, 167, 157-170.

See Also

confusion()

Evaluate the Sparsity Estimation Based on FNR

Description

This function computes false negative rate (FNR) for sparse element of the true coefficients given threshold.

Usage

conf_fnr(x, y, ...)

## S3 method for class 'summary.bvharsp'
conf_fnr(x, y, truth_thr = 0, ...)

Arguments

Details

False negative rate (FNR) is computed by

FNR = \frac{FN}{TP + FN}

where TP is true positive, and FN is false negative.

Value

FNR value in confusion table

References

Bai, R., & Ghosh, M. (2018). High-dimensional multivariate posterior consistency under global-local shrinkage priors. Journal of Multivariate Analysis, 167, 157-170.

See Also

confusion()

Evaluate the Sparsity Estimation Based on F1 Score

Description

This function computes F1 score for sparse element of the true coefficients given threshold.

Usage

conf_fscore(x, y, ...)

## S3 method for class 'summary.bvharsp'
conf_fscore(x, y, truth_thr = 0, ...)

Arguments

Details

The F1 score is computed by

F_1 = \frac{2 precision \times recall}{precision + recall}

Value

F1 score in confusion table

See Also

confusion()

Evaluate the Sparsity Estimation Based on Precision

Description

This function computes precision for sparse element of the true coefficients given threshold.

Usage

conf_prec(x, y, ...)

## S3 method for class 'summary.bvharsp'
conf_prec(x, y, truth_thr = 0, ...)

Arguments

Details

If the element of the estimate \hat\Phi is smaller than some threshold, it is treated to be zero. Then the precision is computed by

precision = \frac{TP}{TP + FP}

where TP is true positive, and FP is false positive.

Value

Precision value in confusion table

References

Bai, R., & Ghosh, M. (2018). High-dimensional multivariate posterior consistency under global-local shrinkage priors. Journal of Multivariate Analysis, 167, 157-170.

See Also

confusion()

Evaluate the Sparsity Estimation Based on Recall

Description

This function computes recall for sparse element of the true coefficients given threshold.

Usage

conf_recall(x, y, ...)

## S3 method for class 'summary.bvharsp'
conf_recall(x, y, truth_thr = 0L, ...)

Arguments

Details

Precision is computed by

recall = \frac{TP}{TP + FN}

where TP is true positive, and FN is false negative.

Value

Recall value in confusion table

References

Bai, R., & Ghosh, M. (2018). High-dimensional multivariate posterior consistency under global-local shrinkage priors. Journal of Multivariate Analysis, 167, 157-170.

See Also

confusion()

Evaluate the Sparsity Estimation Based on Confusion Matrix

Description

This function computes FDR (false discovery rate) and FNR (false negative rate) for sparse element of the true coefficients given threshold.

Usage

confusion(x, y, ...)

## S3 method for class 'summary.bvharsp'
confusion(x, y, truth_thr = 0, ...)

Arguments

Details

When using this function, the true coefficient matrix \Phi should be sparse.

In this confusion matrix, positive (0) means sparsity. FP is false positive, and TP is true positive. FN is false negative, and FN is false negative.

Value

Confusion table as following.

References

Bai, R., & Ghosh, M. (2018). High-dimensional multivariate posterior consistency under global-local shrinkage priors. Journal of Multivariate Analysis, 167, 157-170.

Split a Time Series Dataset into Train-Test Set

Description

Split a given time series dataset into train and test set for evaluation.

Usage

divide_ts(y, n_ahead)

Arguments

Value

List of two datasets, train and test.

Dynamic Spillover

Description

This function gives connectedness table with h-step ahead normalized spillover index (a.k.a. variance shares).

Usage

dynamic_spillover(object, n_ahead = 10L, ...)

## S3 method for class 'bvhardynsp'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

## S3 method for class 'bvhardynsp'
knit_print(x, ...)

## S3 method for class 'olsmod'
dynamic_spillover(object, n_ahead = 10L, window, num_thread = 1, ...)

## S3 method for class 'normaliw'
dynamic_spillover(
  object,
  n_ahead = 10L,
  window,
  num_iter = 1000L,
  num_burn = floor(num_iter/2),
  thinning = 1,
  num_thread = 1,
  ...
)

## S3 method for class 'ldltmod'
dynamic_spillover(
  object,
  n_ahead = 10L,
  window,
  level = 0.05,
  sparse = FALSE,
  num_thread = 1,
  ...
)

## S3 method for class 'svmod'
dynamic_spillover(
  object,
  n_ahead = 10L,
  level = 0.05,
  sparse = FALSE,
  num_thread = 1,
  ...
)

Arguments

References

Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of forecasting, 28(1), 57-66.

CBOE ETF Volatility Index Dataset

Description

Chicago Board Options Exchage (CBOE) Exchange Traded Funds (ETFs) volatility index from FRED.

Usage

etf_vix

Format

A data frame of 1006 row and 9 columns:

From 2012-01-09 to 2015-06-27, 33 missing observations were interpolated by stats::approx() with linear.

GVZCLS

Gold ETF volatility index

VXFXICLS

China ETF volatility index

OVXCLS

Crude Oil ETF volatility index

VXEEMCLS

Emerging Markets ETF volatility index

EVZCLS

EuroCurrency ETF volatility index

VXSLVCLS

Silver ETF volatility index

VXGDXCLS

Gold Miners ETF volatility index

VXXLECLS

Energy Sector ETF volatility index

VXEWZCLS

Brazil ETF volatility index

Details

Copyright, 2016, Chicago Board Options Exchange, Inc.

Note that, in this data frame, dates column is removed. This dataset interpolated 36 missing observations (nontrading dates) using imputeTS::na_interpolation().

Source

Source: https://www.cboe.com

Release: https://www.cboe.com/us/options/market_statistics/daily/

References

Chicago Board Options Exchange, CBOE Gold ETF Volatility Index (GVZCLS), retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/GVZCLS, July 31, 2021.

Chicago Board Options Exchange, CBOE China ETF Volatility Index (VXFXICLS), retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/VXFXICLS, August 1, 2021.

Chicago Board Options Exchange, CBOE Crude Oil ETF Volatility Index (OVXCLS), retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/OVXCLS, August 1, 2021.

Chicago Board Options Exchange, CBOE Emerging Markets ETF Volatility Index (VXEEMCLS), retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/VXEEMCLS, August 1, 2021.

Chicago Board Options Exchange, CBOE EuroCurrency ETF Volatility Index (EVZCLS), retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/EVZCLS, August 2, 2021.

Chicago Board Options Exchange, CBOE Silver ETF Volatility Index (VXSLVCLS), retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/VXSLVCLS, August 1, 2021.

Chicago Board Options Exchange, CBOE Gold Miners ETF Volatility Index (VXGDXCLS), retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/VXGDXCLS, August 1, 2021.

Chicago Board Options Exchange, CBOE Energy Sector ETF Volatility Index (VXXLECLS), retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/VXXLECLS, August 1, 2021.

Chicago Board Options Exchange, CBOE Brazil ETF Volatility Index (VXEWZCLS), retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/VXEWZCLS, August 2, 2021.

Time points and Financial Events

Description

This page describes about some important financial events in 20th century. This might give some hint when cutting data and why we provides datasets in limited period.

Usage

trading_day

Format

A vector trading_day saves dates of etf_vix.

Outline

• 2000: Dot-com bubble

• 2001: September 11 terror and Enron scandal

• 2003: Iraq war (until 2011)

• 2007 to 2008: Financial crisis (US)

  • 2007: Subprime morgage crisis

  • 2008: Bankrupcy of Lehman Brothers

• 2010 to 2016: European sovereign dept crisis

  • 2010: Greek debt crisis

  • 2011: Italian default

  • 2015: Greek default

  • 2016: Brexit

• 2018: US-China trade war

• 2019: Brexit

• 2020: COVID-19

About Datasets in this package

etf_vix ranges from 2012-01-09 to 2015-06-27 (only weekdays). Each year corresponds to Italian default and Grexit. If you wonder the exact vector of the date, see trading_day vector.

Notice

If you want other time period, see codes in the Github repo for the dataset: ygeunkim/bvhar/data-raw/etf_vix.R

You can download what you want by changing a few lines.

Fitted Matrix from Multivariate Time Series Models

Description

By defining stats::fitted() for each model, this function returns fitted matrix.

Usage

## S3 method for class 'varlse'
fitted(object, ...)

## S3 method for class 'vharlse'
fitted(object, ...)

## S3 method for class 'bvarmn'
fitted(object, ...)

## S3 method for class 'bvarflat'
fitted(object, ...)

## S3 method for class 'bvharmn'
fitted(object, ...)

Arguments

Value

matrix object.

Out-of-sample Forecasting based on Expanding Window

Description

This function conducts expanding window forecasting.

Usage

forecast_expand(
  object,
  n_ahead,
  y_test,
  level = 0.05,
  newxreg = NULL,
  num_thread = 1,
  ...
)

## S3 method for class 'olsmod'
forecast_expand(
  object,
  n_ahead,
  y_test,
  level = 0.05,
  newxreg = NULL,
  num_thread = 1,
  ...
)

## S3 method for class 'normaliw'
forecast_expand(
  object,
  n_ahead,
  y_test,
  level = 0.05,
  newxreg = NULL,
  num_thread = 1,
  use_fit = TRUE,
  ...
)

## S3 method for class 'ldltmod'
forecast_expand(
  object,
  n_ahead,
  y_test,
  level = 0.05,
  newxreg = NULL,
  num_thread = 1,
  stable = FALSE,
  sparse = FALSE,
  med = FALSE,
  lpl = FALSE,
  mcmc = TRUE,
  use_fit = TRUE,
  verbose = FALSE,
  ...
)

## S3 method for class 'svmod'
forecast_expand(
  object,
  n_ahead,
  y_test,
  level = 0.05,
  newxreg = NULL,
  num_thread = 1,
  use_sv = TRUE,
  stable = FALSE,
  sparse = FALSE,
  med = FALSE,
  lpl = FALSE,
  mcmc = TRUE,
  use_fit = TRUE,
  verbose = FALSE,
  ...
)

Arguments

Details

Expanding windows forecasting fixes the starting period. It moves the window ahead and forecast h-ahead in y_test set.

Value

predbvhar_expand class

References

Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and practice (3rd ed.). OTEXTS. https://otexts.com/fpp3/

Out-of-sample Forecasting based on Rolling Window

Description

This function conducts rolling window forecasting.

Usage

forecast_roll(
  object,
  n_ahead,
  y_test,
  level = 0.05,
  newxreg = NULL,
  num_thread = 1,
  ...
)

## S3 method for class 'bvharcv'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

is.bvharcv(x)

## S3 method for class 'bvharcv'
knit_print(x, ...)

## S3 method for class 'olsmod'
forecast_roll(
  object,
  n_ahead,
  y_test,
  level = 0.05,
  newxreg = NULL,
  num_thread = 1,
  ...
)

## S3 method for class 'normaliw'
forecast_roll(
  object,
  n_ahead,
  y_test,
  level = 0.05,
  newxreg = NULL,
  num_thread = 1,
  use_fit = TRUE,
  ...
)

## S3 method for class 'ldltmod'
forecast_roll(
  object,
  n_ahead,
  y_test,
  level = 0.05,
  newxreg = NULL,
  num_thread = 1,
  stable = FALSE,
  sparse = FALSE,
  med = FALSE,
  lpl = FALSE,
  mcmc = TRUE,
  use_fit = TRUE,
  verbose = FALSE,
  ...
)

## S3 method for class 'svmod'
forecast_roll(
  object,
  n_ahead,
  y_test,
  level = 0.05,
  newxreg = NULL,
  num_thread = 1,
  use_sv = TRUE,
  stable = FALSE,
  sparse = FALSE,
  med = FALSE,
  lpl = FALSE,
  mcmc = TRUE,
  use_fit = TRUE,
  verbose = FALSE,
  ...
)

Arguments

Details

Rolling windows forecasting fixes window size. It moves the window ahead and forecast h-ahead in y_test set.

Value

predbvhar_roll class

References

Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and practice (3rd ed.). OTEXTS.

Evaluate the Estimation Based on Frobenius Norm

Description

This function computes estimation error given estimated model and true coefficient.

Usage

fromse(x, y, ...)

## S3 method for class 'bvharsp'
fromse(x, y, ...)

Arguments

Details

Consider the Frobenius Norm \lVert \cdot \rVert_F. let \hat{\Phi} be nrow x k the estimates, and let \Phi be the true coefficients matrix. Then the function computes estimation error by

MSE = 100 \frac{\lVert \hat{\Phi} - \Phi \rVert_F}{nrow \times k}

Value

Frobenius norm value

References

Bai, R., & Ghosh, M. (2018). High-dimensional multivariate posterior consistency under global-local shrinkage priors. Journal of Multivariate Analysis, 167, 157-170.

Adding Test Data Layer

Description

This function adds a layer of test dataset.

Usage

geom_eval(data, colour = "red", ...)

Arguments

Value

A ggplot layer

Compare Lists of Models

Description

Draw plot of test error for given models

Usage

gg_loss(
  mod_list,
  y,
  type = c("mse", "mae", "mape", "mase"),
  mean_line = FALSE,
  line_param = list(),
  mean_param = list(),
  viridis = FALSE,
  viridis_option = "D",
  NROW = NULL,
  NCOL = NULL,
  ...
)

Arguments

Value

A ggplot object

See Also

• mse() to compute MSE for given forecast result

• mae() to compute MAE for given forecast result

• mape() to compute MAPE for given forecast result

• mase() to compute MASE for given forecast result

Impulse Response Analysis

Description

Computes responses to impulses or orthogonal impulses

Usage

## S3 method for class 'varlse'
irf(object, lag_max = 10, orthogonal = TRUE, impulse_var, response_var, ...)

## S3 method for class 'vharlse'
irf(object, lag_max = 10, orthogonal = TRUE, impulse_var, response_var, ...)

## S3 method for class 'bvharirf'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

irf(object, lag_max, orthogonal, impulse_var, response_var, ...)

is.bvharirf(x)

## S3 method for class 'bvharirf'
knit_print(x, ...)

Arguments

Value

bvharirf class

Responses to forecast errors

If orthogonal = FALSE, the function gives W_j VMA representation of the process such that

Y_t = \sum_{j = 0}^\infty W_j \epsilon_{t - j}

Responses to orthogonal impulses

If orthogonal = TRUE, it gives orthogonalized VMA representation

\Theta

. Based on variance decomposition (Cholesky decomposition)

\Sigma = P P^T

where P is lower triangular matrix, impulse response analysis if performed under MA representation

y_t = \sum_{i = 0}^\infty \Theta_i v_{t - i}

Here,

\Theta_i = W_i P

and v_t = P^{-1} \epsilon_t are orthogonal.

References

Lütkepohl, H. (2007). New Introduction to Multiple Time Series Analysis. Springer Publishing.

See Also

VARtoVMA()

VHARtoVMA()

Stability of the process

Description

Check the stability condition of coefficient matrix.

Usage

is.stable(x, ...)

## S3 method for class 'varlse'
is.stable(x, ...)

## S3 method for class 'vharlse'
is.stable(x, ...)

## S3 method for class 'bvarmn'
is.stable(x, ...)

## S3 method for class 'bvarflat'
is.stable(x, ...)

## S3 method for class 'bvharmn'
is.stable(x, ...)

Arguments

Details

VAR(p) is stable if

\det(I_m - A z) \neq 0

for \lvert z \rvert \le 1.

Value

logical class

logical class

References

Lütkepohl, H. (2007). New Introduction to Multiple Time Series Analysis. Springer Publishing.

Evaluate the Model Based on MAE (Mean Absolute Error)

Description

This function computes MAE given prediction result versus evaluation set.

Usage

mae(x, y, ...)

## S3 method for class 'predbvhar'
mae(x, y, ...)

## S3 method for class 'bvharcv'
mae(x, y, ...)

Arguments

Details

Let e_t = y_t - \hat{y}_t. MAE is defined by

MSE = mean(\lvert e_t \rvert)

Some researchers prefer MAE to MSE because it is less sensitive to outliers.

Value

MAE vector corressponding to each variable.

References

Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688.

Evaluate the Model Based on MAPE (Mean Absolute Percentage Error)

Description

This function computes MAPE given prediction result versus evaluation set.

Usage

mape(x, y, ...)

## S3 method for class 'predbvhar'
mape(x, y, ...)

## S3 method for class 'bvharcv'
mape(x, y, ...)

Arguments

Details

Let e_t = y_t - \hat{y}_t. Percentage error is defined by p_t = 100 e_t / Y_t (100 can be omitted since comparison is the focus).

MAPE = mean(\lvert p_t \rvert)

Value

MAPE vector corresponding to each variable.

References

Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688.

Evaluate the Model Based on MASE (Mean Absolute Scaled Error)

Description

This function computes MASE given prediction result versus evaluation set.

Usage

mase(x, y, ...)

## S3 method for class 'predbvhar'
mase(x, y, ...)

## S3 method for class 'bvharcv'
mase(x, y, ...)

Arguments

Details

Let e_t = y_t - \hat{y}_t. Scaled error is defined by

q_t = \frac{e_t}{\sum_{i = 2}^{n} \lvert Y_i - Y_{i - 1} \rvert / (n - 1)}

so that the error can be free of the data scale. Then

MASE = mean(\lvert q_t \rvert)

Here, Y_i are the points in the sample, i.e. errors are scaled by the in-sample mean absolute error (mean(\lvert e_t \rvert)) from the naive random walk forecasting.

Value

MASE vector corresponding to each variable.

References

Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688.

Evaluate the Model Based on MRAE (Mean Relative Absolute Error)

Description

This function computes MRAE given prediction result versus evaluation set.

Usage

mrae(x, pred_bench, y, ...)

## S3 method for class 'predbvhar'
mrae(x, pred_bench, y, ...)

## S3 method for class 'bvharcv'
mrae(x, pred_bench, y, ...)

Arguments

Details

Let e_t = y_t - \hat{y}_t. MRAE implements benchmark model as scaling method. Relative error is defined by

r_t = \frac{e_t}{e_t^{\ast}}

where e_t^\ast is the error from the benchmark method. Then

MRAE = mean(\lvert r_t \rvert)

Value

MRAE vector corresponding to each variable.

References

Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688.

Evaluate the Model Based on MSE (Mean Square Error)

Description

This function computes MSE given prediction result versus evaluation set.

Usage

mse(x, y, ...)

## S3 method for class 'predbvhar'
mse(x, y, ...)

## S3 method for class 'bvharcv'
mse(x, y, ...)

Arguments

Details

Let e_t = y_t - \hat{y}_t. Then

MSE = mean(e_t^2)

MSE is the most used accuracy measure.

Value

MSE vector corresponding to each variable.

References

Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688.

Forecasting Multivariate Time Series

Description

Forecasts multivariate time series using given model.

Usage

## S3 method for class 'varlse'
predict(object, n_ahead, level = 0.05, newxreg, ...)

## S3 method for class 'vharlse'
predict(object, n_ahead, level = 0.05, newxreg, ...)

## S3 method for class 'bvarmn'
predict(object, n_ahead, n_iter = 100L, level = 0.05, num_thread = 1, ...)

## S3 method for class 'bvharmn'
predict(object, n_ahead, n_iter = 100L, level = 0.05, num_thread = 1, ...)

## S3 method for class 'bvarflat'
predict(object, n_ahead, n_iter = 100L, level = 0.05, num_thread = 1, ...)

## S3 method for class 'bvarldlt'
predict(
  object,
  n_ahead,
  level = 0.05,
  newxreg,
  stable = FALSE,
  num_thread = 1,
  sparse = FALSE,
  med = FALSE,
  warn = FALSE,
  ...
)

## S3 method for class 'bvharldlt'
predict(
  object,
  n_ahead,
  level = 0.05,
  newxreg,
  stable = FALSE,
  num_thread = 1,
  sparse = FALSE,
  med = FALSE,
  warn = FALSE,
  ...
)

## S3 method for class 'bvarsv'
predict(
  object,
  n_ahead,
  level = 0.05,
  newxreg,
  stable = FALSE,
  num_thread = 1,
  use_sv = TRUE,
  sparse = FALSE,
  med = FALSE,
  warn = FALSE,
  ...
)

## S3 method for class 'bvharsv'
predict(
  object,
  n_ahead,
  level = 0.05,
  newxreg,
  stable = FALSE,
  num_thread = 1,
  use_sv = TRUE,
  sparse = FALSE,
  med = FALSE,
  warn = FALSE,
  ...
)

## S3 method for class 'predbvhar'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

is.predbvhar(x)

## S3 method for class 'predbvhar'
knit_print(x, ...)

Arguments

Value

predbvhar class with the following components:

process

object$process

forecast

forecast matrix

se

standard error matrix

lower

lower confidence interval

upper

upper confidence interval

lower_joint

lower CI adjusted (Bonferroni)

upper_joint

upper CI adjusted (Bonferroni)

y

object$y

n-step ahead forecasting VAR(p)

See pp35 of Lütkepohl (2007). Consider h-step ahead forecasting (e.g. n + 1, ... n + h).

Let y_{(n)}^T = (y_n^T, ..., y_{n - p + 1}^T, 1). Then one-step ahead (point) forecasting:

\hat{y}_{n + 1}^T = y_{(n)}^T \hat{B}

Recursively, let \hat{y}_{(n + 1)}^T = (\hat{y}_{n + 1}^T, y_n^T, ..., y_{n - p + 2}^T, 1). Then two-step ahead (point) forecasting:

\hat{y}_{n + 2}^T = \hat{y}_{(n + 1)}^T \hat{B}

Similarly, h-step ahead (point) forecasting:

\hat{y}_{n + h}^T = \hat{y}_{(n + h - 1)}^T \hat{B}

How about confident region? Confidence interval at h-period is

y_{k,t}(h) \pm z_(\alpha / 2) \sigma_k (h)

Joint forecast region of 100(1-\alpha)% can be computed by

\{ (y_{k, 1}, y_{k, h}) \mid y_{k, n}(i) - z_{(\alpha / 2h)} \sigma_n(i) \le y_{n, i} \le y_{k, n}(i) + z_{(\alpha / 2h)} \sigma_k(i), i = 1, \ldots, h \}

See the pp41 of Lütkepohl (2007).

To compute covariance matrix, it needs VMA representation:

Y_{t}(h) = c + \sum_{i = h}^{\infty} W_{i} \epsilon_{t + h - i} = c + \sum_{i = 0}^{\infty} W_{h + i} \epsilon_{t - i}

Then

\Sigma_y(h) = MSE [ y_t(h) ] = \sum_{i = 0}^{h - 1} W_i \Sigma_{\epsilon} W_i^T = \Sigma_y(h - 1) + W_{h - 1} \Sigma_{\epsilon} W_{h - 1}^T

n-step ahead forecasting VHAR

Let T_{HAR} is VHAR linear transformation matrix. Since VHAR is the linearly transformed VAR(22), let y_{(n)}^T = (y_n^T, y_{n - 1}^T, ..., y_{n - 21}^T, 1).

Then one-step ahead (point) forecasting:

\hat{y}_{n + 1}^T = y_{(n)}^T T_{HAR} \hat{\Phi}

Recursively, let \hat{y}_{(n + 1)}^T = (\hat{y}_{n + 1}^T, y_n^T, ..., y_{n - 20}^T, 1). Then two-step ahead (point) forecasting:

\hat{y}_{n + 2}^T = \hat{y}_{(n + 1)}^T T_{HAR} \hat{\Phi}

and h-step ahead (point) forecasting:

\hat{y}_{n + h}^T = \hat{y}_{(n + h - 1)}^T T_{HAR} \hat{\Phi}

n-step ahead forecasting BVAR(p) with minnesota prior

Point forecasts are computed by posterior mean of the parameters. See Section 3 of Bańbura et al. (2010).

Let \hat{B} be the posterior MN mean and let \hat{V} be the posterior MN precision.

Then predictive posterior for each step

y_{n + 1} \mid \Sigma_e, y \sim N( vec(y_{(n)}^T A), \Sigma_e \otimes (1 + y_{(n)}^T \hat{V}^{-1} y_{(n)}) )

y_{n + 2} \mid \Sigma_e, y \sim N( vec(\hat{y}_{(n + 1)}^T A), \Sigma_e \otimes (1 + \hat{y}_{(n + 1)}^T \hat{V}^{-1} \hat{y}_{(n + 1)}) )

and recursively,

y_{n + h} \mid \Sigma_e, y \sim N( vec(\hat{y}_{(n + h - 1)}^T A), \Sigma_e \otimes (1 + \hat{y}_{(n + h - 1)}^T \hat{V}^{-1} \hat{y}_{(n + h - 1)}) )

n-step ahead forecasting BVHAR

Let \hat\Phi be the posterior MN mean and let \hat\Psi be the posterior MN precision.

Then predictive posterior for each step

y_{n + 1} \mid \Sigma_e, y \sim N( vec(y_{(n)}^T \tilde{T}^T \Phi), \Sigma_e \otimes (1 + y_{(n)}^T \tilde{T} \hat\Psi^{-1} \tilde{T} y_{(n)}) )

y_{n + 2} \mid \Sigma_e, y \sim N( vec(y_{(n + 1)}^T \tilde{T}^T \Phi), \Sigma_e \otimes (1 + y_{(n + 1)}^T \tilde{T} \hat\Psi^{-1} \tilde{T} y_{(n + 1)}) )

and recursively,

y_{n + h} \mid \Sigma_e, y \sim N( vec(y_{(n + h - 1)}^T \tilde{T}^T \Phi), \Sigma_e \otimes (1 + y_{(n + h - 1)}^T \tilde{T} \hat\Psi^{-1} \tilde{T} y_{(n + h - 1)}) )

References

Lütkepohl, H. (2007). New Introduction to Multiple Time Series Analysis. Springer Publishing.

Corsi, F. (2008). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174-196.

Baek, C. and Park, M. (2021). Sparse vector heterogeneous autoregressive modeling for realized volatility. J. Korean Stat. Soc. 50, 495-510.

Bańbura, M., Giannone, D., & Reichlin, L. (2010). Large Bayesian vector auto regressions. Journal of Applied Econometrics, 25(1).

Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2013). Bayesian data analysis. Chapman and Hall/CRC.

Karlsson, S. (2013). Chapter 15 Forecasting with Bayesian Vector Autoregression. Handbook of Economic Forecasting, 2, 791-897.

Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions: Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25.

Ghosh, S., Khare, K., & Michailidis, G. (2018). High-Dimensional Posterior Consistency in Bayesian Vector Autoregressive Models. Journal of the American Statistical Association, 114(526).

Korobilis, D. (2013). VAR FORECASTING USING BAYESIAN VARIABLE SELECTION. Journal of Applied Econometrics, 28(2).

Korobilis, D. (2013). VAR FORECASTING USING BAYESIAN VARIABLE SELECTION. Journal of Applied Econometrics, 28(2).

Huber, F., Koop, G., & Onorante, L. (2021). Inducing Sparsity and Shrinkage in Time-Varying Parameter Models. Journal of Business & Economic Statistics, 39(3), 669-683.

Summarizing BVAR and BVHAR with Shrinkage Priors

Description

Conduct variable selection.

Usage

## S3 method for class 'summary.bvharsp'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

## S3 method for class 'summary.bvharsp'
knit_print(x, ...)

## S3 method for class 'ssvsmod'
summary(object, method = c("pip", "ci"), threshold = 0.5, level = 0.05, ...)

## S3 method for class 'hsmod'
summary(object, method = c("pip", "ci"), threshold = 0.5, level = 0.05, ...)

## S3 method for class 'ngmod'
summary(object, level = 0.05, ...)

Arguments

Value

summary.ssvsmod object

hsmod object

ngmod object

References

George, E. I., & McCulloch, R. E. (1993). Variable Selection via Gibbs Sampling. Journal of the American Statistical Association, 88(423), 881-889.

George, E. I., Sun, D., & Ni, S. (2008). Bayesian stochastic search for VAR model restrictions. Journal of Econometrics, 142(1), 553-580.

Koop, G., & Korobilis, D. (2009). Bayesian Multivariate Time Series Methods for Empirical Macroeconomics. Foundations and Trends® in Econometrics, 3(4), 267-358.

O’Hara, R. B., & Sillanpää, M. J. (2009). A review of Bayesian variable selection methods: what, how and which. Bayesian Analysis, 4(1), 85-117.

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

ggplot2

autolayer, autoplot

Evaluate the Model Based on RelMAE (Relative MAE)

Description

This function computes RelMAE given prediction result versus evaluation set.

Usage

relmae(x, pred_bench, y, ...)

## S3 method for class 'predbvhar'
relmae(x, pred_bench, y, ...)

## S3 method for class 'bvharcv'
relmae(x, pred_bench, y, ...)

Arguments

Details

Let e_t = y_t - \hat{y}_t. RelMAE implements MAE of benchmark model as relative measures. Let MAE_b be the MAE of the benchmark model. Then

RelMAE = \frac{MAE}{MAE_b}

where MAE is the MAE of our model.

Value

RelMAE vector corresponding to each variable.

References

Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688.

Evaluate the Estimation Based on Relative Spectral Norm Error

Description

This function computes relative estimation error given estimated model and true coefficient.

Usage

relspne(x, y, ...)

## S3 method for class 'bvharsp'
relspne(x, y, ...)

Arguments

Details

Let \lVert \cdot \rVert_2 be the spectral norm of a matrix, let \hat{\Phi} be the estimates, and let \Phi be the true coefficients matrix. Then the function computes relative estimation error by

\frac{\lVert \hat{\Phi} - \Phi \rVert_2}{\lVert \Phi \rVert_2}

Value

Spectral norm value

References

Ghosh, S., Khare, K., & Michailidis, G. (2018). High-Dimensional Posterior Consistency in Bayesian Vector Autoregressive Models. Journal of the American Statistical Association, 114(526).

Residual Matrix from Multivariate Time Series Models

Description

By defining stats::residuals() for each model, this function returns residual.

Usage

## S3 method for class 'varlse'
residuals(object, ...)

## S3 method for class 'vharlse'
residuals(object, ...)

## S3 method for class 'bvarmn'
residuals(object, ...)

## S3 method for class 'bvarflat'
residuals(object, ...)

## S3 method for class 'bvharmn'
residuals(object, ...)

Arguments

Value

matrix object.

Evaluate the Model Based on RMAFE

Description

This function computes RMAFE (Mean Absolute Forecast Error Relative to the Benchmark)

Usage

rmafe(x, pred_bench, y, ...)

## S3 method for class 'predbvhar'
rmafe(x, pred_bench, y, ...)

## S3 method for class 'bvharcv'
rmafe(x, pred_bench, y, ...)

Arguments

Details

Let e_t = y_t - \hat{y}_t. RMAFE is the ratio of L1 norm of e_t from forecasting object and from benchmark model.

RMAFE = \frac{sum(\lVert e_t \rVert)}{sum(\lVert e_t^{(b)} \rVert)}

where e_t^{(b)} is the error from the benchmark model.

Value

RMAFE vector corresponding to each variable.

References

Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688.

Bańbura, M., Giannone, D., & Reichlin, L. (2010). Large Bayesian vector auto regressions. Journal of Applied Econometrics, 25(1).

Ghosh, S., Khare, K., & Michailidis, G. (2018). High-Dimensional Posterior Consistency in Bayesian Vector Autoregressive Models. Journal of the American Statistical Association, 114(526).

Evaluate the Model Based on RMAPE (Relative MAPE)

Description

This function computes RMAPE given prediction result versus evaluation set.

Usage

rmape(x, pred_bench, y, ...)

## S3 method for class 'predbvhar'
rmape(x, pred_bench, y, ...)

## S3 method for class 'bvharcv'
rmape(x, pred_bench, y, ...)

Arguments

Details

RMAPE is the ratio of MAPE of given model and the benchmark one. Let MAPE_b be the MAPE of the benchmark model. Then

RMAPE = \frac{mean(MAPE)}{mean(MAPE_b)}

where MAPE is the MAPE of our model.

Value

RMAPE vector corresponding to each variable.

References

Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688.

Evaluate the Model Based on RMASE (Relative MASE)

Description

This function computes RMASE given prediction result versus evaluation set.

Usage

rmase(x, pred_bench, y, ...)

## S3 method for class 'predbvhar'
rmase(x, pred_bench, y, ...)

## S3 method for class 'bvharcv'
rmase(x, pred_bench, y, ...)

Arguments

Details

RMASE is the ratio of MAPE of given model and the benchmark one. Let MASE_b be the MAPE of the benchmark model. Then

RMASE = \frac{mean(MASE)}{mean(MASE_b)}

where MASE is the MASE of our model.

Value

RMASE vector corresponding to each variable.

References

Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688.

Evaluate the Model Based on RMSFE

Description

This function computes RMSFE (Mean Squared Forecast Error Relative to the Benchmark)

Usage

rmsfe(x, pred_bench, y, ...)

## S3 method for class 'predbvhar'
rmsfe(x, pred_bench, y, ...)

## S3 method for class 'bvharcv'
rmsfe(x, pred_bench, y, ...)

Arguments

Details

Let e_t = y_t - \hat{y}_t. RMSFE is the ratio of L2 norm of e_t from forecasting object and from benchmark model.

RMSFE = \frac{sum(\lVert e_t \rVert)}{sum(\lVert e_t^{(b)} \rVert)}

where e_t^{(b)} is the error from the benchmark model.

Value

RMSFE vector corresponding to each variable.

References

Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688.

Bańbura, M., Giannone, D., & Reichlin, L. (2010). Large Bayesian vector auto regressions. Journal of Applied Econometrics, 25(1).

Ghosh, S., Khare, K., & Michailidis, G. (2018). High-Dimensional Posterior Consistency in Bayesian Vector Autoregressive Models. Journal of the American Statistical Association, 114(526).

Hyperparameters for Bayesian Models

Description

Set hyperparameters of Bayesian VAR and VHAR models.

Usage

set_bvar(sigma, lambda = 0.1, delta, eps = 1e-04)

set_bvar_flat(U)

set_bvhar(sigma, lambda = 0.1, delta, eps = 1e-04)

set_weight_bvhar(sigma, lambda = 0.1, eps = 1e-04, daily, weekly, monthly)

## S3 method for class 'bvharspec'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

is.bvharspec(x)

## S3 method for class 'bvharspec'
knit_print(x, ...)

Arguments

Details

• Missing arguments will be set to be default values in each model function mentioned above.

• set_bvar() sets hyperparameters for bvar_minnesota().

• Each delta (vector), lambda (length of 1), sigma (vector), eps (vector) corresponds to \delta_j, \lambda, \delta_j, \epsilon.

\delta_i are related to the belief to random walk.

• If \delta_i = 1 for all i, random walk prior

• If \delta_i = 0 for all i, white noise prior

\lambda controls the overall tightness of the prior around these two prior beliefs.

• If \lambda = 0, the posterior is equivalent to prior and the data do not influence the estimates.

• If \lambda = \infty, the posterior mean becomes OLS estimates (VAR).

\sigma_i^2 / \sigma_j^2 in Minnesota moments explain the data scales.

• set_bvar_flat sets hyperparameters for bvar_flat().

• set_bvhar() sets hyperparameters for bvhar_minnesota() with VAR-type Minnesota prior, i.e. BVHAR-S model.

• set_weight_bvhar() sets hyperparameters for bvhar_minnesota() with VHAR-type Minnesota prior, i.e. BVHAR-L model.

Value

Every function returns bvharspec class. It is the list of which the components are the same as the arguments provided. If the argument is not specified, NULL is assigned here. The default values mentioned above will be considered in each fitting function.

process

Model name: BVAR, BVHAR

prior

Prior name: Minnesota (Minnesota prior for BVAR), Hierarchical (Hierarchical prior for BVAR), MN_VAR (BVHAR-S), MN_VHAR (BVHAR-L), Flat (Flat prior for BVAR)

sigma

Vector value (or bvharpriorspec class) assigned for sigma

lambda

Value (or bvharpriorspec class) assigned for lambda

delta

Vector value assigned for delta

eps

Value assigned for epsilon

set_weight_bvhar() has different component with delta due to its different construction.

daily

Vector value assigned for daily weight

weekly

Vector value assigned for weekly weight

monthly

Vector value assigned for monthly weight

Note

By using set_psi() and set_lambda() each, hierarchical modeling is available.

References

Bańbura, M., Giannone, D., & Reichlin, L. (2010). Large Bayesian vector auto regressions. Journal of Applied Econometrics, 25(1).

Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions: Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25.

Ghosh, S., Khare, K., & Michailidis, G. (2018). High-Dimensional Posterior Consistency in Bayesian Vector Autoregressive Models. Journal of the American Statistical Association, 114(526).

Kim, Y. G., and Baek, C. (2024). Bayesian vector heterogeneous autoregressive modeling. Journal of Statistical Computation and Simulation, 94(6), 1139-1157.

Kim, Y. G., and Baek, C. (2024). Bayesian vector heterogeneous autoregressive modeling. Journal of Statistical Computation and Simulation, 94(6), 1139-1157.

See Also

• lambda hyperprior specification set_lambda()

• sigma hyperprior specification set_psi()

Examples

# Minnesota BVAR specification------------------------
bvar_spec <- set_bvar(
  sigma = c(.03, .02, .01), # Sigma = diag(.03^2, .02^2, .01^2)
  lambda = .2, # lambda = .2
  delta = rep(.1, 3), # delta1 = .1, delta2 = .1, delta3 = .1
  eps = 1e-04 # eps = 1e-04
)
class(bvar_spec)
str(bvar_spec)
# Flat BVAR specification-------------------------
# 3-dim
# p = 5 with constant term
# U = 500 * I(mp + 1)
bvar_flat_spec <- set_bvar_flat(U = 500 * diag(16))
class(bvar_flat_spec)
str(bvar_flat_spec)
# BVHAR-S specification-----------------------
bvhar_var_spec <- set_bvhar(
  sigma = c(.03, .02, .01), # Sigma = diag(.03^2, .02^2, .01^2)
  lambda = .2, # lambda = .2
  delta = rep(.1, 3), # delta1 = .1, delta2 = .1, delta3 = .1
  eps = 1e-04 # eps = 1e-04
)
class(bvhar_var_spec)
str(bvhar_var_spec)
# BVHAR-L specification---------------------------
bvhar_vhar_spec <- set_weight_bvhar(
  sigma = c(.03, .02, .01), # Sigma = diag(.03^2, .02^2, .01^2)
  lambda = .2, # lambda = .2
  eps = 1e-04, # eps = 1e-04
  daily = rep(.2, 3), # daily1 = .2, daily2 = .2, daily3 = .2
  weekly = rep(.1, 3), # weekly1 = .1, weekly2 = .1, weekly3 = .1
  monthly = rep(.05, 3) # monthly1 = .05, monthly2 = .05, monthly3 = .05
)
class(bvhar_vhar_spec)
str(bvhar_vhar_spec)

Dirichlet-Laplace Hyperparameter for Coefficients and Contemporaneous Coefficients

Description

Set DL hyperparameters for VAR or VHAR coefficient and contemporaneous coefficient.

Usage

set_dl(dir_grid = 100L, shape = 0.01, scale = 0.01)

## S3 method for class 'dlspec'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

is.dlspec(x)

Arguments

Value

dlspec object

References

Bhattacharya, A., Pati, D., Pillai, N. S., & Dunson, D. B. (2015). Dirichlet-Laplace Priors for Optimal Shrinkage. Journal of the American Statistical Association, 110(512), 1479-1490.

Korobilis, D., & Shimizu, K. (2022). Bayesian Approaches to Shrinkage and Sparse Estimation. Foundations and Trends® in Econometrics, 11(4), 230-354.

Generalized Double Pareto Shrinkage Hyperparameters for Coefficients and Contemporaneous Coefficients

Description

Set GDP hyperparameters for VAR or VHAR coefficient and contemporaneous coefficient.

Usage

set_gdp(shape_grid = 100L, rate_grid = 100L)

is.gdpspec(x)

Arguments

Value

gdpspec object

References

Armagan, A., Dunson, D. B., & Lee, J. (2013). GENERALIZED DOUBLE PARETO SHRINKAGE. Statistica Sinica, 23(1), 119–143.

Korobilis, D., & Shimizu, K. (2022). Bayesian Approaches to Shrinkage and Sparse Estimation. Foundations and Trends® in Econometrics, 11(4), 230-354.

Horseshoe Prior Specification

Description

Set initial hyperparameters and parameter before starting Gibbs sampler for Horseshoe prior.

Usage

set_horseshoe(local_sparsity = 1, group_sparsity = 1, global_sparsity = 1)

## S3 method for class 'horseshoespec'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

is.horseshoespec(x)

## S3 method for class 'horseshoespec'
knit_print(x, ...)

Arguments

Details

Set horseshoe prior initialization for VAR family.

• local_sparsity: Initial local shrinkage

• group_sparsity: Initial group shrinkage

• global_sparsity: Initial global shrinkage

In this package, horseshoe prior model is estimated by Gibbs sampling, initial means initial values for that gibbs sampler.

References

Carvalho, C. M., Polson, N. G., & Scott, J. G. (2010). The horseshoe estimator for sparse signals. Biometrika, 97(2), 465-480.

Makalic, E., & Schmidt, D. F. (2016). A Simple Sampler for the Horseshoe Estimator. IEEE Signal Processing Letters, 23(1), 179-182.

Prior for Constant Term

Description

Set Normal prior hyperparameters for constant term

Usage

set_intercept(mean = 0, sd = 0.1)

## S3 method for class 'interceptspec'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

is.interceptspec(x)

## S3 method for class 'interceptspec'
knit_print(x, ...)

Arguments

Hyperpriors for Bayesian Models

Description

Set hyperpriors of Bayesian VAR and VHAR models.

Usage

set_lambda(
  mode = 0.2,
  sd = 0.4,
  param = NULL,
  lower = 1e-05,
  upper = 3,
  grid_size = 100L
)

set_psi(shape = 4e-04, scale = 4e-04, lower = 1e-05, upper = 3)

## S3 method for class 'bvharpriorspec'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

is.bvharpriorspec(x)

## S3 method for class 'bvharpriorspec'
knit_print(x, ...)

Arguments

Details

In addition to Normal-IW priors set_bvar(), set_bvhar(), and set_weight_bvhar(), these functions give hierarchical structure to the model.

• set_lambda() specifies hyperprior for \lambda (lambda), which is Gamma distribution.

• set_psi() specifies hyperprior for \psi / (\nu_0 - k - 1) = \sigma^2 (sigma), which is Inverse gamma distribution.

The following set of ⁠(mode, sd)⁠ are recommended by Sims and Zha (1998) for set_lambda().

• ⁠(mode = .2, sd = .4)⁠: default

• ⁠(mode = 1, sd = 1)⁠

Giannone et al. (2015) suggested data-based selection for set_psi(). It chooses (0.02)^2 based on its empirical data set.

Value

bvharpriorspec object

References

Giannone, D., Lenza, M., & Primiceri, G. E. (2015). Prior Selection for Vector Autoregressions. Review of Economics and Statistics, 97(2).

Examples

# Hirearchical BVAR specification------------------------
set_bvar(
  sigma = set_psi(shape = 4e-4, scale = 4e-4),
  lambda = set_lambda(mode = .2, sd = .4),
  delta = rep(1, 3),
  eps = 1e-04 # eps = 1e-04
)

Covariance Matrix Prior Specification

Description

Set prior for covariance matrix.

Usage

set_ldlt(ig_shape = 3, ig_scl = 0.01)

set_sv(ig_shape = 3, ig_scl = 0.01, initial_mean = 1, initial_prec = 0.1)

## S3 method for class 'covspec'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

is.covspec(x)

is.svspec(x)

is.ldltspec(x)

Arguments

Details

set_ldlt() specifies LDLT of precision matrix,

\Sigma^{-1} = L^T D^{-1} L

set_sv() specifices time varying precision matrix under stochastic volatility framework based on

\Sigma_t^{-1} = L^T D_t^{-1} L

References

Carriero, A., Chan, J., Clark, T. E., & Marcellino, M. (2022). Corrigendum to “Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors” [J. Econometrics 212 (1)(2019) 137-154]. Journal of Econometrics, 227(2), 506-512.

Chan, J., Koop, G., Poirier, D., & Tobias, J. (2019). Bayesian Econometric Methods (2nd ed., Econometric Exercises). Cambridge: Cambridge University Press.

Normal-Gamma Hyperparameter for Coefficients and Contemporaneous Coefficients

Description

Set NG hyperparameters for VAR or VHAR coefficient and contemporaneous coefficient.

Usage

set_ng(
  shape_sd = 0.01,
  group_shape = 0.01,
  group_scale = 0.01,
  global_shape = 0.01,
  global_scale = 0.01
)

## S3 method for class 'ngspec'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

is.ngspec(x)

Arguments

Value

ngspec object

References

Chan, J. C. C. (2021). Minnesota-type adaptive hierarchical priors for large Bayesian VARs. International Journal of Forecasting, 37(3), 1212-1226.

Huber, F., & Feldkircher, M. (2019). Adaptive Shrinkage in Bayesian Vector Autoregressive Models. Journal of Business & Economic Statistics, 37(1), 27-39.

Korobilis, D., & Shimizu, K. (2022). Bayesian Approaches to Shrinkage and Sparse Estimation. Foundations and Trends® in Econometrics, 11(4), 230-354.

Stochastic Search Variable Selection (SSVS) Hyperparameter for Coefficients Matrix and Cholesky Factor

Description

Set SSVS hyperparameters for VAR or VHAR coefficient matrix and Cholesky factor.

Usage

set_ssvs(
  spike_grid = 100L,
  slab_shape = 0.01,
  slab_scl = 0.01,
  s1 = c(1, 1),
  s2 = c(1, 1),
  shape = 0.01,
  rate = 0.01
)

## S3 method for class 'ssvsinput'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

is.ssvsinput(x)

## S3 method for class 'ssvsinput'
knit_print(x, ...)

Arguments

Details

Let \alpha be the vectorized coefficient, \alpha = vec(A). Spike-slab prior is given using two normal distributions.

\alpha_j \mid \gamma_j \sim (1 - \gamma_j) N(0, \tau_{0j}^2) + \gamma_j N(0, \tau_{1j}^2)

As spike-slab prior itself suggests, set \tau_{0j} small (point mass at zero: spike distribution) and set \tau_{1j} large (symmetric by zero: slab distribution).

\gamma_j is the proportion of the nonzero coefficients and it follows

\gamma_j \sim Bernoulli(p_j)

• coef_spike: \tau_{0j}

• coef_slab: \tau_{1j}

• coef_mixture: p_j

• j = 1, \ldots, mk: vectorized format corresponding to coefficient matrix

• If one value is provided, model function will read it by replicated value.

• coef_non: vectorized constant term is given prior Normal distribution with variance cI. Here, coef_non is \sqrt{c}.

Next for precision matrix \Sigma_e^{-1}, SSVS applies Cholesky decomposition.

\Sigma_e^{-1} = \Psi \Psi^T

where \Psi = \{\psi_{ij}\} is upper triangular.

Diagonal components follow the gamma distribution.

\psi_{jj}^2 \sim Gamma(shape = a_j, rate = b_j)

For each row of off-diagonal (upper-triangular) components, we apply spike-slab prior again.

\psi_{ij} \mid w_{ij} \sim (1 - w_{ij}) N(0, \kappa_{0,ij}^2) + w_{ij} N(0, \kappa_{1,ij}^2)

w_{ij} \sim Bernoulli(q_{ij})

• shape: a_j

• rate: b_j

• chol_spike: \kappa_{0,ij}

• chol_slab: \kappa_{1,ij}

• chol_mixture: q_{ij}

• j = 1, \ldots, mk: vectorized format corresponding to coefficient matrix

• i = 1, \ldots, j - 1 and j = 2, \ldots, m: \eta = (\psi_{12}, \psi_{13}, \psi_{23}, \psi_{14}, \ldots, \psi_{34}, \ldots, \psi_{1m}, \ldots, \psi_{m - 1, m})^T

• chol_ arguments can be one value for replication, vector, or upper triangular matrix.

Value

ssvsinput object

References

George, E. I., & McCulloch, R. E. (1993). Variable Selection via Gibbs Sampling. Journal of the American Statistical Association, 88(423), 881-889.

George, E. I., Sun, D., & Ni, S. (2008). Bayesian stochastic search for VAR model restrictions. Journal of Econometrics, 142(1), 553-580.

Ishwaran, H., & Rao, J. S. (2005). Spike and slab variable selection: Frequentist and Bayesian strategies. The Annals of Statistics, 33(2).

Koop, G., & Korobilis, D. (2009). Bayesian Multivariate Time Series Methods for Empirical Macroeconomics. Foundations and Trends® in Econometrics, 3(4), 267-358.

Generate Inverse-Wishart Random Matrix

Description

This function samples one matrix IW matrix.

Usage

sim_iw(mat_scale, shape)

Arguments

Details

Consider \Sigma \sim IW(\Psi, \nu).

1. Upper triangular Bartlett decomposition: k x k matrix Q = [q_{ij}] upper triangular with

   1. q_{ii}^2 \chi_{\nu - i + 1}^2

   2. q_{ij} \sim N(0, 1) with i < j (upper triangular)

2. Lower triangular Cholesky decomposition: \Psi = L L^T

3. A = L (Q^{-1})^T

4. \Sigma = A A^T \sim IW(\Psi, \nu)

Value

One k x k matrix following IW distribution

Generate Matrix Normal Random Matrix

Description

This function samples one matrix gaussian matrix.

Usage

sim_matgaussian(mat_mean, mat_scale_u, mat_scale_v, u_prec)

Arguments

Details

Consider n x k matrix Y_1, \ldots, Y_n \sim MN(M, U, V) where M is n x k, U is n x n, and V is k x k.

1. Lower triangular Cholesky decomposition: U = P P^T and V = L L^T

2. Standard normal generation: s x m matrix Z_i = [z_{ij} \sim N(0, 1)] in row-wise direction.

3. Y_i = M + P Z_i L^T

This function only generates one matrix, i.e. Y_1.

Value

One n x k matrix following MN distribution.

Generate Minnesota BVAR Parameters

Description

This function generates parameters of BVAR with Minnesota prior.

Usage

sim_mncoef(p, bayes_spec = set_bvar(), full = TRUE)

Arguments

Details

Implementing dummy observation constructions, Bańbura et al. (2010) sets Normal-IW prior.

A \mid \Sigma_e \sim MN(A_0, \Omega_0, \Sigma_e)

\Sigma_e \sim IW(S_0, \alpha_0)

If full = FALSE, the result of \Sigma_e is the same as input (diag(sigma)).

Value

List with the following component.

coefficients

BVAR coefficient (MN)

covmat

BVAR variance (IW or diagonal matrix of sigma of bayes_spec)

References

Bańbura, M., Giannone, D., & Reichlin, L. (2010). Large Bayesian vector auto regressions. Journal of Applied Econometrics, 25(1).

Karlsson, S. (2013). Chapter 15 Forecasting with Bayesian Vector Autoregression. Handbook of Economic Forecasting, 2, 791-897.

Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions: Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25.

See Also

• set_bvar() to specify the hyperparameters of Minnesota prior.

Examples

# Generate (A, Sigma)
# BVAR(p = 2)
# sigma: 1, 1, 1
# lambda: .1
# delta: .1, .1, .1
# epsilon: 1e-04
set.seed(1)
sim_mncoef(
  p = 2,
  bayes_spec = set_bvar(
    sigma = rep(1, 3),
    lambda = .1,
    delta = rep(.1, 3),
    eps = 1e-04
  ),
  full = TRUE
)

Generate Normal-IW Random Family

Description

This function samples normal inverse-wishart matrices.

Usage

sim_mniw(num_sim, mat_mean, mat_scale_u, mat_scale, shape, u_prec = FALSE)

Arguments

Details

Consider (Y_i, \Sigma_i) \sim MIW(M, U, \Psi, \nu).

1. Generate upper triangular factor of \Sigma_i = C_i C_i^T in the upper triangular Bartlett decomposition.

2. Standard normal generation: n x k matrix Z_i = [z_{ij} \sim N(0, 1)] in row-wise direction.

3. Lower triangular Cholesky decomposition: U = P P^T

4. A_i = M + P Z_i C_i^T

Generate Multivariate Normal Random Vector

Description

This function samples n x muti-dimensional normal random matrix.

Usage

sim_mnormal(
  num_sim,
  mu = rep(0, 5),
  sig = diag(5),
  method = c("eigen", "chol")
)

Arguments

Details

Consider x_1, \ldots, x_n \sim N_m (\mu, \Sigma).

1. Lower triangular Cholesky decomposition: \Sigma = L L^T

2. Standard normal generation: Z_{i1}, Z_{in} \stackrel{iid}{\sim} N(0, 1)

3. Z_i = (Z_{i1}, \ldots, Z_{in})^T

4. X_i = L Z_i + \mu

Value

T x k matrix

Generate Minnesota BVAR Parameters

Description

This function generates parameters of BVAR with Minnesota prior.

Usage

sim_mnvhar_coef(bayes_spec = set_bvhar(), full = TRUE)

Arguments

Details

Normal-IW family for vector HAR model:

\Phi \mid \Sigma_e \sim MN(M_0, \Omega_0, \Sigma_e)

\Sigma_e \sim IW(\Psi_0, \nu_0)

Value

List with the following component.

coefficients

BVHAR coefficient (MN)

covmat

BVHAR variance (IW or diagonal matrix of sigma of bayes_spec)

References

Kim, Y. G., and Baek, C. (2024). Bayesian vector heterogeneous autoregressive modeling. Journal of Statistical Computation and Simulation, 94(6), 1139-1157.

See Also

• set_bvhar() to specify the hyperparameters of VAR-type Minnesota prior.

• set_weight_bvhar() to specify the hyperparameters of HAR-type Minnesota prior.

Examples

# Generate (Phi, Sigma)
# BVHAR-S
# sigma: 1, 1, 1
# lambda: .1
# delta: .1, .1, .1
# epsilon: 1e-04
set.seed(1)
sim_mnvhar_coef(
  bayes_spec = set_bvhar(
    sigma = rep(1, 3),
    lambda = .1,
    delta = rep(.1, 3),
    eps = 1e-04
  ),
  full = TRUE
)

Generate Multivariate t Random Vector

Description

This function samples n x multi-dimensional t-random matrix.

Usage

sim_mvt(num_sim, df, mu, sig, method = c("eigen", "chol"))

Arguments

Value

T x k matrix

Generate Multivariate Time Series Process Following VAR(p)

Description

This function generates multivariate time series dataset that follows VAR(p).

Usage

sim_var(
  num_sim,
  num_burn,
  var_coef,
  var_lag,
  sig_error = diag(ncol(var_coef)),
  init = matrix(0L, nrow = var_lag, ncol = ncol(var_coef)),
  method = c("eigen", "chol"),
  process = c("gaussian", "student"),
  t_param = 5
)

Arguments

Details

1. Generate \epsilon_1, \epsilon_n \sim N(0, \Sigma)

2. For i = 1, ... n,

   y_{p + i} = (y_{p + i - 1}^T, \ldots, y_i^T, 1)^T B + \epsilon_i

3. Then the output is (y_{p + 1}, \ldots, y_{n + p})^T

Initial values might be set to be zero vector or (I_m - A_1 - \cdots - A_p)^{-1} c.

Value

T x k matrix

References

Lütkepohl, H. (2007). New Introduction to Multiple Time Series Analysis. Springer Publishing.

Generate Multivariate Time Series Process Following VAR(p)

Description

This function generates multivariate time series dataset that follows VAR(p).

Usage

sim_vhar(
  num_sim,
  num_burn,
  vhar_coef,
  week = 5L,
  month = 22L,
  sig_error = diag(ncol(vhar_coef)),
  init = matrix(0L, nrow = month, ncol = ncol(vhar_coef)),
  method = c("eigen", "chol"),
  process = c("gaussian", "student"),
  t_param = 5
)

Arguments

Details

Let M be the month order, e.g. M = 22.

1. Generate \epsilon_1, \epsilon_n \sim N(0, \Sigma)

2. For i = 1, ... n,

   y_{M + i} = (y_{M + i - 1}^T, \ldots, y_i^T, 1)^T C_{HAR}^T \Phi + \epsilon_i

3. Then the output is (y_{M + 1}, \ldots, y_{n + M})^T

4. For i = 1, ... n,

   y_{p + i} = (y_{p + i - 1}^T, \ldots, y_i^T, 1)^T B + \epsilon_i

5. Then the output is (y_{p + 1}, \ldots, y_{n + p})^T

Initial values might be set to be zero vector or (I_m - A_1 - \cdots - A_p)^{-1} c.

Value

T x k matrix

References

Lütkepohl, H. (2007). New Introduction to Multiple Time Series Analysis. Springer Publishing.

h-step ahead Normalized Spillover

Description

This function gives connectedness table with h-step ahead normalized spillover index (a.k.a. variance shares).

Usage

spillover(object, n_ahead = 10L, ...)

## S3 method for class 'bvharspillover'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

## S3 method for class 'bvharspillover'
knit_print(x, ...)

## S3 method for class 'olsmod'
spillover(object, n_ahead = 10L, ...)

## S3 method for class 'normaliw'
spillover(
  object,
  n_ahead = 10L,
  num_iter = 5000L,
  num_burn = floor(num_iter/2),
  thinning = 1L,
  ...
)

## S3 method for class 'bvarldlt'
spillover(object, n_ahead = 10L, level = 0.05, sparse = FALSE, ...)

## S3 method for class 'bvharldlt'
spillover(object, n_ahead = 10L, level = 0.05, sparse = FALSE, ...)

Arguments

References

Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of forecasting, 28(1), 57-66.

Evaluate the Estimation Based on Spectral Norm Error

Description

This function computes estimation error given estimated model and true coefficient.

Usage

spne(x, y, ...)

## S3 method for class 'bvharsp'
spne(x, y, ...)

Arguments

Details

Let \lVert \cdot \rVert_2 be the spectral norm of a matrix, let \hat{\Phi} be the estimates, and let \Phi be the true coefficients matrix. Then the function computes estimation error by

\lVert \hat{\Phi} - \Phi \rVert_2

Value

Spectral norm value

References

Ghosh, S., Khare, K., & Michailidis, G. (2018). High-Dimensional Posterior Consistency in Bayesian Vector Autoregressive Models. Journal of the American Statistical Association, 114(526).

Roots of characteristic polynomial

Description

Compute the character polynomial of coefficient matrix.

Usage

stableroot(x, ...)

## S3 method for class 'varlse'
stableroot(x, ...)

## S3 method for class 'vharlse'
stableroot(x, ...)

## S3 method for class 'bvarmn'
stableroot(x, ...)

## S3 method for class 'bvarflat'
stableroot(x, ...)

## S3 method for class 'bvharmn'
stableroot(x, ...)

Arguments

Details

To know whether the process is stable or not, make characteristic polynomial.

\det(I_m - A z) = 0

where A is VAR(1) coefficient matrix representation.

Value

Numeric vector.

References

Lütkepohl, H. (2007). New Introduction to Multiple Time Series Analysis. Springer Publishing.

Summarizing Bayesian Multivariate Time Series Model

Description

summary method for normaliw class.

Usage

## S3 method for class 'normaliw'
summary(
  object,
  num_chains = 1,
  num_iter = 1000,
  num_burn = floor(num_iter/2),
  thinning = 1,
  verbose = FALSE,
  num_thread = 1,
  ...
)

## S3 method for class 'summary.normaliw'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

## S3 method for class 'summary.normaliw'
knit_print(x, ...)

Arguments

Details

From Minnesota prior, set of coefficient matrices and residual covariance matrix have matrix Normal Inverse-Wishart distribution.

BVAR:

(A, \Sigma_e) \sim MNIW(\hat{A}, \hat{V}^{-1}, \hat\Sigma_e, \alpha_0 + n)

where \hat{V} = X_\ast^T X_\ast is the posterior precision of MN.

BVHAR:

(\Phi, \Sigma_e) \sim MNIW(\hat\Phi, \hat{V}_H^{-1}, \hat\Sigma_e, \nu + n)

where \hat{V}_H = X_{+}^T X_{+} is the posterior precision of MN.

Value

summary.normaliw class has the following components:

names

Variable names

totobs

Total number of the observation

obs

Sample size used when training = totobs - p

p

Lag of VAR

m

Dimension of the data

call

Matched call

spec

Model specification (bvharspec)

mn_mean

MN Mean of posterior distribution (MN-IW)

mn_prec

MN Precision of posterior distribution (MN-IW)

iw_scale

IW scale of posterior distribution (MN-IW)

iw_shape

IW df of posterior distribution (MN-IW)

iter

Number of MCMC iterations

burn

Number of MCMC burn-in

thin

MCMC thinning

alpha_record (BVAR) and phi_record (BVHAR)

MCMC record of coefficients vector

psi_record

MCMC record of upper cholesky factor

omega_record

MCMC record of diagonal of cholesky factor

eta_record

MCMC record of upper part of cholesky factor

param

MCMC record of every parameter

coefficients

Posterior mean of coefficients

covmat

Posterior mean of covariance

References

Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions: Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25.

Bańbura, M., Giannone, D., & Reichlin, L. (2010). Large Bayesian vector auto regressions. Journal of Applied Econometrics, 25(1).

Summarizing Vector Autoregressive Model

Description

summary method for varlse class.

Usage

## S3 method for class 'varlse'
summary(object, ...)

## S3 method for class 'summary.varlse'
print(x, digits = max(3L, getOption("digits") - 3L), signif_code = TRUE, ...)

## S3 method for class 'summary.varlse'
knit_print(x, ...)

Arguments

Value

summary.varlse class additionally computes the following

• AIC - AIC

• BIC - BIC

• HQ - HQ

• FPE - FPE

References

Lütkepohl, H. (2007). New Introduction to Multiple Time Series Analysis. Springer Publishing.

Summarizing Vector HAR Model

Description

summary method for vharlse class.

Usage

## S3 method for class 'vharlse'
summary(object, ...)

## S3 method for class 'summary.vharlse'
print(x, digits = max(3L, getOption("digits") - 3L), signif_code = TRUE, ...)

## S3 method for class 'summary.vharlse'
knit_print(x, ...)

Arguments

Value

summary.vharlse class additionally computes the following

• AIC - AIC

• BIC - BIC

• HQ - HQ

• FPE - FPE

References

Lütkepohl, H. (2007). New Introduction to Multiple Time Series Analysis. Springer Publishing.

Corsi, F. (2008). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174-196.

Baek, C. and Park, M. (2021). Sparse vector heterogeneous autoregressive modeling for realized volatility. J. Korean Stat. Soc. 50, 495-510.

Fitting Bayesian VAR with Coefficient and Covariance Prior

Description

This function fits BVAR. Covariance term can be homoskedastic or heteroskedastic (stochastic volatility). It can have Minnesota, SSVS, and Horseshoe prior.

Usage

var_bayes(
  y,
  p,
  exogen = NULL,
  s = 0,
  num_chains = 1,
  num_iter = 1000,
  num_burn = floor(num_iter/2),
  thinning = 1,
  coef_spec = set_bvar(),
  contem_spec = coef_spec,
  cov_spec = set_ldlt(),
  intercept = set_intercept(),
  exogen_spec = coef_spec,
  include_mean = TRUE,
  minnesota = TRUE,
  ggl = TRUE,
  save_init = FALSE,
  convergence = NULL,
  verbose = FALSE,
  num_thread = 1
)

## S3 method for class 'bvarsv'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

## S3 method for class 'bvarldlt'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

## S3 method for class 'bvarsv'
knit_print(x, ...)

## S3 method for class 'bvarldlt'
knit_print(x, ...)

Arguments

Details

Cholesky stochastic volatility modeling for VAR based on

\Sigma_t^{-1} = L^T D_t^{-1} L

, and implements corrected triangular algorithm for Gibbs sampler.

Value

var_bayes() returns an object named bvarsv class.

coefficients

Posterior mean of coefficients.

chol_posterior

Posterior mean of contemporaneous effects.

param

Every set of MCMC trace.

param_names

Name of every parameter.

group

Indicators for group.

num_group

Number of groups.

df

Numer of Coefficients: ⁠3m + 1⁠ or ⁠3m⁠

p

VAR lag

m

Dimension of the data

obs

Sample size used when training = totobs - p

totobs

Total number of the observation

call

Matched call

process

Description of the model, e.g. VHAR_SSVS_SV, VHAR_Horseshoe_SV, or VHAR_minnesota-part_SV

type

include constant term (const) or not (none)

spec

Coefficients prior specification

sv

log volatility prior specification

intercept

Intercept prior specification

init

Initial values

chain

The numer of chains

iter

Total iterations

burn

Burn-in

thin

Thinning

y0

Y_0

design

X_0

y

Raw input

If it is SSVS or Horseshoe:

pip

Posterior inclusion probabilities.

References

Carriero, A., Chan, J., Clark, T. E., & Marcellino, M. (2022). Corrigendum to “Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors” [J. Econometrics 212 (1)(2019) 137-154]. Journal of Econometrics, 227(2), 506-512.

Chan, J., Koop, G., Poirier, D., & Tobias, J. (2019). Bayesian Econometric Methods (2nd ed., Econometric Exercises). Cambridge: Cambridge University Press.

Cogley, T., & Sargent, T. J. (2005). Drifts and volatilities: monetary policies and outcomes in the post WWII US. Review of Economic Dynamics, 8(2), 262-302.

Gruber, L., & Kastner, G. (2022). Forecasting macroeconomic data with Bayesian VARs: Sparse or dense? It depends! arXiv.

Huber, F., Koop, G., & Onorante, L. (2021). Inducing Sparsity and Shrinkage in Time-Varying Parameter Models. Journal of Business & Economic Statistics, 39(3), 669-683.

Korobilis, D., & Shimizu, K. (2022). Bayesian Approaches to Shrinkage and Sparse Estimation. Foundations and Trends® in Econometrics, 11(4), 230-354.

Ray, P., & Bhattacharya, A. (2018). Signal Adaptive Variable Selector for the Horseshoe Prior. arXiv.

Fitting Vector Autoregressive Model of Order p Model

Description

This function fits VAR(p) using OLS method.

Usage

var_lm(
  y,
  p = 1,
  exogen = NULL,
  s = 0,
  include_mean = TRUE,
  method = c("nor", "chol", "qr")
)

## S3 method for class 'varlse'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

## S3 method for class 'varlse'
logLik(object, ...)

## S3 method for class 'varlse'
AIC(object, ...)

## S3 method for class 'varlse'
BIC(object, ...)

is.varlse(x)

is.bvharmod(x)

## S3 method for class 'varlse'
knit_print(x, ...)

Arguments

Details

This package specifies VAR(p) model as

Y_{t} = A_1 Y_{t - 1} + \cdots + A_p Y_{t - p} + c + \epsilon_t

If include_type = TRUE, there is constant term. The function estimates every coefficient matrix.

Consider the response matrix Y_0. Let T be the total number of sample, let m be the dimension of the time series, let p be the order of the model, and let n = T - p. Likelihood of VAR(p) has

Y_0 \mid B, \Sigma_e \sim MN(X_0 B, I_s, \Sigma_e)

where X_0 is the design matrix, and MN is matrix normal distribution.

Then log-likelihood of vector autoregressive model family is specified by

\log p(Y_0 \mid B, \Sigma_e) = - \frac{nm}{2} \log 2\pi - \frac{n}{2} \log \det \Sigma_e - \frac{1}{2} tr( (Y_0 - X_0 B) \Sigma_e^{-1} (Y_0 - X_0 B)^T )

In addition, recall that the OLS estimator for the matrix coefficient matrix is the same as MLE under the Gaussian assumption. MLE for \Sigma_e has different denominator, n.

\hat{B} = \hat{B}^{LS} = \hat{B}^{ML} = (X_0^T X_0)^{-1} X_0^T Y_0

\hat\Sigma_e = \frac{1}{s - k} (Y_0 - X_0 \hat{B})^T (Y_0 - X_0 \hat{B})

\tilde\Sigma_e = \frac{1}{s} (Y_0 - X_0 \hat{B})^T (Y_0 - X_0 \hat{B}) = \frac{s - k}{s} \hat\Sigma_e

Let \tilde{\Sigma}_e be the MLE and let \hat{\Sigma}_e be the unbiased estimator (covmat) for \Sigma_e. Note that

\tilde{\Sigma}_e = \frac{n - k}{n} \hat{\Sigma}_e

Then

AIC(p) = \log \det \Sigma_e + \frac{2}{n}(\text{number of freely estimated parameters})

where the number of freely estimated parameters is mk, i.e. pm^2 or pm^2 + m.

Let \tilde{\Sigma}_e be the MLE and let \hat{\Sigma}_e be the unbiased estimator (covmat) for \Sigma_e. Note that

\tilde{\Sigma}_e = \frac{n - k}{T} \hat{\Sigma}_e

Then

BIC(p) = \log \det \Sigma_e + \frac{\log n}{n}(\text{number of freely estimated parameters})

where the number of freely estimated parameters is pm^2.

Value

var_lm() returns an object named varlse class. It is a list with the following components:

coefficients

Coefficient Matrix

fitted.values

Fitted response values

residuals

Residuals

covmat

LS estimate for covariance matrix

df

Numer of Coefficients

p

Lag of VAR

m

Dimension of the data

obs

Sample size used when training = totobs - p

totobs

Total number of the observation

call

Matched call

process

Process: VAR

type

include constant term (const) or not (none)

design

Design matrix

y

Raw input

y0

Multivariate response matrix

method

Solving method

call

Matched call

It is also a bvharmod class.

References

Lütkepohl, H. (2007). New Introduction to Multiple Time Series Analysis. Springer Publishing.

Akaike, H. (1969). Fitting autoregressive models for prediction. Ann Inst Stat Math 21, 243-247.

Akaike, H. (1971). Autoregressive model fitting for control. Ann Inst Stat Math 23, 163-180.

Akaike H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, vol. 19, no. 6, pp. 716-723.

Akaike H. (1998). Information Theory and an Extension of the Maximum Likelihood Principle. In: Parzen E., Tanabe K., Kitagawa G. (eds) Selected Papers of Hirotugu Akaike. Springer Series in Statistics (Perspectives in Statistics). Springer, New York, NY.

Gideon Schwarz. (1978). Estimating the Dimension of a Model. Ann. Statist. 6 (2) 461 - 464.

See Also

• summary.varlse() to summarize VAR model

Examples

# Perform the function using etf_vix dataset
fit <- var_lm(y = etf_vix, p = 2)
class(fit)
str(fit)

# Extract coef, fitted values, and residuals
coef(fit)
head(residuals(fit))
head(fitted(fit))

Fitting Bayesian VHAR with Coefficient and Covariance Prior

Description

This function fits BVHAR. Covariance term can be homoskedastic or heteroskedastic (stochastic volatility). It can have Minnesota, SSVS, and Horseshoe prior.

Usage

vhar_bayes(
  y,
  har = c(5, 22),
  exogen = NULL,
  s = 0,
  num_chains = 1,
  num_iter = 1000,
  num_burn = floor(num_iter/2),
  thinning = 1,
  coef_spec = set_bvhar(),
  contem_spec = coef_spec,
  cov_spec = set_ldlt(),
  intercept = set_intercept(),
  exogen_spec = coef_spec,
  include_mean = TRUE,
  minnesota = c("longrun", "short", "no"),
  ggl = TRUE,
  save_init = FALSE,
  convergence = NULL,
  verbose = FALSE,
  num_thread = 1
)

## S3 method for class 'bvharsv'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

## S3 method for class 'bvharldlt'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

## S3 method for class 'bvharsv'
knit_print(x, ...)

## S3 method for class 'bvharldlt'
knit_print(x, ...)

Arguments

Details

Cholesky stochastic volatility modeling for VHAR based on

\Sigma_t^{-1} = L^T D_t^{-1} L

Value

vhar_bayes() returns an object named bvharsv class. It is a list with the following components:

coefficients

Posterior mean of coefficients.

chol_posterior

Posterior mean of contemporaneous effects.

param

Every set of MCMC trace.

param_names

Name of every parameter.

group

Indicators for group.

num_group

Number of groups.

df

Numer of Coefficients: ⁠3m + 1⁠ or ⁠3m⁠

p

3 (The number of terms. It contains this element for usage in other functions.)

week

Order for weekly term

month

Order for monthly term

m

Dimension of the data

obs

Sample size used when training = totobs - p

totobs

Total number of the observation

call

Matched call

process

Description of the model, e.g. VHAR_SSVS_SV, VHAR_Horseshoe_SV, or VHAR_minnesota-part_SV

type

include constant term (const) or not (none)

spec

Coefficients prior specification

sv

log volatility prior specification

init

Initial values

intercept

Intercept prior specification

chain

The numer of chains

iter

Total iterations

burn

Burn-in

thin

Thinning

HARtrans

VHAR linear transformation matrix

y0

Y_0

design

X_0

y

Raw input

If it is SSVS or Horseshoe:

pip

Posterior inclusion probabilities.

References

Kim, Y. G., and Baek, C. (2024). Bayesian vector heterogeneous autoregressive modeling. Journal of Statistical Computation and Simulation, 94(6), 1139-1157.

Kim, Y. G., and Baek, C. (n.d.). Working paper.

Fitting Vector Heterogeneous Autoregressive Model

Description

This function fits VHAR using OLS method.

Usage

vhar_lm(
  y,
  har = c(5, 22),
  exogen = NULL,
  s = 0,
  include_mean = TRUE,
  method = c("nor", "chol", "qr")
)

## S3 method for class 'vharlse'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

## S3 method for class 'vharlse'
logLik(object, ...)

## S3 method for class 'vharlse'
AIC(object, ...)

## S3 method for class 'vharlse'
BIC(object, ...)

is.vharlse(x)

## S3 method for class 'vharlse'
knit_print(x, ...)

Arguments

Details

For VHAR model

Y_{t} = \Phi^{(d)} Y_{t - 1} + \Phi^{(w)} Y_{t - 1}^{(w)} + \Phi^{(m)} Y_{t - 1}^{(m)} + \epsilon_t

the function gives basic values.

Value

vhar_lm() returns an object named vharlse class. It is a list with the following components:

coefficients

Coefficient Matrix

fitted.values

Fitted response values

residuals

Residuals

covmat

LS estimate for covariance matrix

df

Numer of Coefficients

m

Dimension of the data

obs

Sample size used when training = totobs - month

y0

Multivariate response matrix

p

3 (The number of terms. vharlse contains this element for usage in other functions.)

week

Order for weekly term

month

Order for monthly term

totobs

Total number of the observation

process

Process: VHAR

type

include constant term (const) or not (none)

HARtrans

VHAR linear transformation matrix

design

Design matrix of VAR(month)

y

Raw input

method

Solving method

call

Matched call

It is also a bvharmod class.

References

Baek, C. and Park, M. (2021). Sparse vector heterogeneous autoregressive modeling for realized volatility. J. Korean Stat. Soc. 50, 495-510.

Bubák, V., Kočenda, E., & Žikeš, F. (2011). Volatility transmission in emerging European foreign exchange markets. Journal of Banking & Finance, 35(11), 2829-2841.

Corsi, F. (2008). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174-196.

See Also

• coef.vharlse(), residuals.vharlse(), and fitted.vharlse()

• summary.vharlse() to summarize VHAR model

Examples

# Perform the function using etf_vix dataset
fit <- vhar_lm(y = etf_vix)
class(fit)
str(fit)

# Extract coef, fitted values, and residuals
coef(fit)
head(residuals(fit))
head(fitted(fit))