Package 'bayesianVARs'

Title:	MCMC Estimation of Bayesian Vectorautoregressions
Description:	Efficient Markov Chain Monte Carlo (MCMC) algorithms for the fully Bayesian estimation of vectorautoregressions (VARs) featuring stochastic volatility (SV). Implements state-of-the-art shrinkage priors following Gruber & Kastner (2025) <doi:10.1016/j.ijforecast.2025.02.001>. Efficient equation-per-equation estimation following Kastner & Huber (2020) <doi:10.1002/for.2680> and Carrerio et al. (2021) <doi:10.1016/j.jeconom.2021.11.010>.
Authors:	Luis Gruber [cph, aut, cre] (ORCID: <https://orcid.org/0000-0002-2399-738X>), Stefan Haan [aut], Gregor Kastner [aut, ths] (ORCID: <https://orcid.org/0000-0002-8237-8271>)
Maintainer:	Luis Gruber <[email protected]>
License:	GPL (>= 3)
Version:	0.1.8.9000
Built:	2026-06-01 09:52:10 UTC
Source:	https://github.com/luisgruber/bayesianvars

Help Index

Extract or Replace Parts of a bayesianVARs_coef object
Extract or Replace Parts of a bayesianVARs_draws object
Markov Chain Monte Carlo Sampling for Bayesian Vectorautoregressions
Extract VAR coefficients
Retrieve the structural parameter \boldsymbol{B}_0 samples from an IRF object.
Simulate fitted/predicted historical values for an estimated VAR model
Impulse response functions
Draw from generalized inverse Gaussian
Pairwise visualization of out-of-sample posterior predictive densities.
Plot method for bayesianVARs_bvar
Visualization of in-sample fit of an estimated VAR.
Impulse Responses Plot
Fan chart
Visualization of the residuals of an estimated VAR.
Posterior heatmaps for matrix valued parameters
Predict method for Bayesian VARs
Pretty printing of a bvar object
Print method for bayesianVARs_predict objects
Print method for summary.bayesianVARs_bvar objects
Print method for summary.bayesianVARs_predict objects
Extract Model Residuals
Specify prior on PHI
Specify prior on Sigma
Set identifying restrictions for the structural VAR parameters.
Stable posterior draws
Summary method for bayesianVARs_bvar objects
Summary statistics for bayesianVARs posterior draws.
Summary method for bayesianVARs_predict objects
Data from the US-economy
Extract posterior draws of the (time-varying) variance-covariance matrix for a VAR model

Extract or Replace Parts of a bayesianVARs_coef object

Description

Extract or replace parts of a bayesianVARs_coef object.

Usage

## S3 method for class 'bayesianVARs_coef'
x[i, j, ...]
## S3 method for class 'bayesianVARs_coef'
x[i, j, ...]

Arguments

x

An object of type bayesianVARs_coef.

i

indices

j

indices

...

further indices

Value

An object of type bayesianVARs_coef.

Examples

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

# Extract coefficients, which are of class bayesianVARs_coef
phi <- coef(mod)
phi[1,1,1]
# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

# Extract coefficients, which are of class bayesianVARs_coef
phi <- coef(mod)
phi[1,1,1]

Extract or Replace Parts of a bayesianVARs_draws object

Description

Extract or replace parts of a bayesianVARs_draws object.

Usage

## S3 method for class 'bayesianVARs_draws'
x[i, j, ...]
## S3 method for class 'bayesianVARs_draws'
x[i, j, ...]

Arguments

x

An object of type bayesianVARs_draws.

i

indices

j

indices

...

further indices

Value

An object of type bayesianVARs_draws.

Examples

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

# Extract coefficients, which are of class bayesianVARs_draws
phi <- coef(mod)
phi[1,1,1]
# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

# Extract coefficients, which are of class bayesianVARs_draws
phi <- coef(mod)
phi[1,1,1]

Markov Chain Monte Carlo Sampling for Bayesian Vectorautoregressions

Description

bvar simulates from the joint posterior distribution of the parameters and latent variables and returns the posterior draws.

Usage

bvar(
  data,
  lags = 1L,
  draws = 1000L,
  burnin = 1000L,
  thin = 1L,
  prior_intercept = 10,
  prior_phi = specify_prior_phi(data = data, lags = lags, prior = "HS"),
  prior_sigma = specify_prior_sigma(data = data, type = "factor", quiet = TRUE),
  sv_keep = "last",
  expert_huge = FALSE,
  quiet = FALSE,
  startvals = list()
)
bvar(
  data,
  lags = 1L,
  draws = 1000L,
  burnin = 1000L,
  thin = 1L,
  prior_intercept = 10,
  prior_phi = specify_prior_phi(data = data, lags = lags, prior = "HS"),
  prior_sigma = specify_prior_sigma(data = data, type = "factor", quiet = TRUE),
  sv_keep = "last",
  expert_huge = FALSE,
  quiet = FALSE,
  startvals = list()
)

Arguments

data

Data matrix (can be a time series object). Each of $M$ columns is assumed to contain a single time-series of length $T$ .

lags

Integer indicating the order of the VAR, i.e. the number of lags of the dependent variables included as predictors.

draws

single integer indicating the number of draws after the burnin

burnin

single integer indicating the number of draws discarded as burnin

thin

single integer. Every $thin$ th draw will be stored. Default is thin=1L.

prior_intercept

Either prior_intercept=FALSE and no constant term (intercept) will be included. Or a numeric vector of length $M$ indicating the (fixed) prior standard deviations on the constant term. A single number will be recycled accordingly. Default is prior_intercept=10.

prior_phi

bayesianVARs_prior_phi object specifying prior for the reduced form VAR coefficients. Best use constructor specify_prior_phi.

prior_sigma

bayesianVARs_prior_sigma object specifying prior for the variance-covariance matrix of the VAR. Best use constructor specify_prior_sigma.

sv_keep

String equal to "all" or "last". In case of sv_keep = "last", the default, only draws for the very last log-variance $h_T$ are stored.

expert_huge

logical value indicating whether to use a specific algorithm for huge VARs. Only possible if a factor structure for the errors is specified. See section MCMC algorithm below for more details.

quiet

logical value indicating whether information about the progress during sampling should be displayed during sampling (default is TRUE).

startvals

optional list with starting values.

Details

The VAR(p) model is of the following form: $\boldsymbol{y}^\prime_t = \boldsymbol{\iota}^\prime + \boldsymbol{x}^\prime_t\boldsymbol{\Phi} + \boldsymbol{\epsilon}^\prime_t$ , where $\boldsymbol{y}_t$ is a $M$ -dimensional vector of dependent variables and $\boldsymbol{\epsilon}_t$ is the error term of the same dimension. $\boldsymbol{x}_t$ is a $K=pM$ -dimensional vector containing lagged/past values of the dependent variables $\boldsymbol{y}_{t-l}$ for $l=1,\dots,p$ and $\boldsymbol{\iota}$ is a constant term (intercept) of dimension $M\times 1$ . The reduced-form coefficient matrix $\boldsymbol{\Phi}$ is of dimension $K \times M$ .

bvar offers two different specifications for the errors: The user can choose between a factor stochastic volatility structure or a cholesky stochastic volatility structure. In both cases the disturbances $\boldsymbol{\epsilon}_t$ are assumed to follow a $M$ -dimensional multivariate normal distribution with zero mean and variance-covariance matrix $\boldsymbol{\Sigma}_t$ . In case of the cholesky specification $\boldsymbol{\Sigma}_t = \boldsymbol{U}^{\prime -1} \boldsymbol{D}_t \boldsymbol{U}^{-1}$ , where $\boldsymbol{U}^{-1}$ is upper unitriangular (with ones on the diagonal). The diagonal matrix $\boldsymbol{D}_t$ depends upon latent log-variances, i.e. $\boldsymbol{D}_t=diag(exp(h_{1t}),\dots, exp(h_{Mt})$ . The log-variances follow a priori independent autoregressive processes $h_{it}\sim N(\mu_i + \phi_i(h_{i,t-1}-\mu_i),\sigma_i^2)$ for $i=1,\dots,M$ . In case of the factor structure, $\boldsymbol{\Sigma}_t = \boldsymbol{\Lambda} \boldsymbol{V}_t \boldsymbol{\Lambda}^\prime + \boldsymbol{G}_t$ . The diagonal matrices $\boldsymbol{V}_t$ and $\boldsymbol{G}_t$ depend upon latent log-variances, i.e. $\boldsymbol{G}_t=diag(exp(h_{1t}),\dots, exp(h_{Mt})$ and $\boldsymbol{V}_t=diag(exp(h_{M+1,t}),\dots, exp(h_{M+r,t})$ . The log-variances follow a priori independent autoregressive processes $h_{it}\sim N(\mu_i + \phi_i(h_{i,t-1}-\mu_i),\sigma_i^2)$ for $i=1,\dots,M$ and $h_{M+j,t}\sim N(\phi_ih_{M+j,t-1},\sigma_{M+j}^2)$ for $j=1,\dots,r$ .

Value

An object of type bayesianVARs_bvar, a list containing the following objects:

PHI: A bayesianVARs_coef object, an array, containing the posterior draws of the VAR coefficients (including the intercept).
U: A bayesianVARs_draws object, a matrix, containing the posterior draws of the contemporaneous coefficients (if cholesky decomposition for sigma is specified).
logvar: A bayesianVARs_draws object containing the log-variance draws.
sv_para: A baysesianVARs_draws object containing the posterior draws of the stochastic volatility related parameters.
phi_hyperparameter: A matrix containing the posterior draws of the hyperparameters of the conditional normal prior on the VAR coefficients.
u_hyperparameter: A matrix containing the posterior draws of the hyperparameters of the conditional normal prior on U (if cholesky decomposition for sigma is specified).
bench: proc_time object containing the run time of the sampler.
V_prior: An array containing the posterior draws of the variances of the conditional normal prior on the VAR coefficients.
facload: A bayesianVARs_draws object, an array, containing draws from the posterior distribution of the factor loadings matrix (if factor decomposition for sigma is specified).
fac: A bayesianVARs_draws object, an array, containing factor draws from the posterior distribution (if factor decomposition for sigma is specified).
Y: Matrix containing the dependent variables used for estimation.
X matrix containing the lagged values of the dependent variables, i.e. the covariates.
lags: Integer indicating the lag order of the VAR.
intercept: Logical indicating whether a constant term is included.
heteroscedastic logical indicating whether heteroscedasticity is assumed.
Yraw: Matrix containing the dependent variables, including the initial 'lags' observations.
Traw: Integer indicating the total number of observations.
sigma_type: Character specifying the decomposition of the variance-covariance matrix.
datamat: Matrix containing both 'Y' and 'X'.
config: List containing information on configuration parameters.

MCMC algorithm

To sample efficiently the reduced-form VAR coefficients assuming a factor structure for the errors, the equation per equation algorithm in Kastner & Huber (2020) is implemented. The factor structure has the advantage that an algorithm for sampling from high-dimensional Gaussian distributions can be exploited by setting expert_huge = TRUE. However, this speeds up computations only if $K > T$ , i.e. the number of coefficients per equations exceeds the number of observations. All parameters and latent variables associated with the factor-structure are sampled using package factorstochvol-package's function update_fsv callable on the C-level only.

To sample efficiently the reduced-form VAR coefficients, assuming a cholesky-structure for the errors, the corrected triangular algorithm in Carriero et al. (2021) is implemented. The SV parameters and latent variables are sampled using package stochvol's update_fast_sv function. The precision parameters, i.e. the free off-diagonal elements in $\boldsymbol{U}$ , can be sampled using Bayesian linear regression methods, see Cogley and Sargent (2005).

References

Gruber, L. and Kastner, G. (2025). Forecasting macroeconomic data with Bayesian VARs: Sparse or dense? It depends! International Journal of Forecasting. doi:10.1016/j.ijforecast.2025.02.001.

Kastner, G. and Huber, F. Sparse (2020). Bayesian vector autoregressions in huge dimensions. Journal of Forecasting. 39, 1142–1165, doi:10.1002/for.2680.

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

Carriero, A. and Chan, J. and Clark, T. E. and Marcellino, M. (2021). Corrigendum to “Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors” [J. Econometrics 212 (1) (2019) 137–154]. Journal of Econometrics, doi:10.1016/j.jeconom.2021.11.010.

Cogley, S. and Sargent, T. (2005). Drifts and volatilities: monetary policies and outcomes in the post WWII US. Review of Economic Dynamics, 8, 262–302, doi:10.1016/j.red.2004.10.009.

Hosszejni, D. and Kastner, G. (2021). Modeling Univariate and Multivariate Stochastic Volatility in R with stochvol and factorstochvol. Journal of Statistical Software, 100, 1–-34. doi:10.18637/jss.v100.i12.

Examples

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

# Plot
plot(mod)

# Summary
summary(mod)

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

# Plot
plot(mod)

# Summary
summary(mod)

Extract VAR coefficients

Description

Extracts posterior draws of the VAR coefficients from a VAR model estimated with bvar().

Usage

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

Arguments

object

A bayesianVARs_bvar object obtained from bvar().

...

Currently ignored.

Value

Returns a numeric array of dimension $M \times K \times draws$ , where M is the number of time-series, K is the number of covariates per equation (including the intercept) and draws is the number of stored posterior draws.

Examples

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

# Extract posterior draws of VAR coefficients
bvar_coefs <- coef(mod)
# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

# Extract posterior draws of VAR coefficients
bvar_coefs <- coef(mod)

Retrieve the structural parameter $\boldsymbol{B}_0$ samples from an IRF object.

Description

Retrieve the structural parameter $\boldsymbol{B}_0$ samples from an IRF object.

Usage

extractB0(x)
extractB0(x)

Arguments

x

a bayesianVARs_irf object

Author(s)

Stefan Haan [email protected]

Examples

train_data <- 100 * usmacro_growth[,c("GDPC1", "GDPCTPI", "GS1", "M2REAL", "CPIAUCSL")]
prior_sigma <- specify_prior_sigma(train_data, type="cholesky", cholesky_heteroscedastic=FALSE)
mod <- bvar(train_data, lags=5L, prior_sigma=prior_sigma, quiet=TRUE)

structural_restrictions <- specify_structural_restrictions(
 mod,
 restrictions_B0=rbind(
   c(1 ,NA,0 ,NA,NA),
   c(0 ,1 ,0 ,NA,NA),
   c(0 ,NA,1 ,NA,NA),
   c(0 ,0 ,NA,1 ,NA),
   c(0 ,0 ,0 ,0 ,1 )
 )
)
irf_structural <- irf(
 mod, ahead=8,
 structural_restrictions=structural_restrictions
)

B0 <- extractB0(irf_structural)

# Visually check that the restriction B0[1,1] >= 0 has been satisfied
hist(
 B0[1,1,],
 xlim=range(0, B0),
 main = paste0("Posterior B0[", 1, ",", 1,"]")
)
abline(v=0, col=2, lwd=2)
train_data <- 100 * usmacro_growth[,c("GDPC1", "GDPCTPI", "GS1", "M2REAL", "CPIAUCSL")]
prior_sigma <- specify_prior_sigma(train_data, type="cholesky", cholesky_heteroscedastic=FALSE)
mod <- bvar(train_data, lags=5L, prior_sigma=prior_sigma, quiet=TRUE)

structural_restrictions <- specify_structural_restrictions(
 mod,
 restrictions_B0=rbind(
   c(1 ,NA,0 ,NA,NA),
   c(0 ,1 ,0 ,NA,NA),
   c(0 ,NA,1 ,NA,NA),
   c(0 ,0 ,NA,1 ,NA),
   c(0 ,0 ,0 ,0 ,1 )
 )
)
irf_structural <- irf(
 mod, ahead=8,
 structural_restrictions=structural_restrictions
)

B0 <- extractB0(irf_structural)

# Visually check that the restriction B0[1,1] >= 0 has been satisfied
hist(
 B0[1,1,],
 xlim=range(0, B0),
 main = paste0("Posterior B0[", 1, ",", 1,"]")
)
abline(v=0, col=2, lwd=2)

Simulate fitted/predicted historical values for an estimated VAR model

Description

Simulates the fitted/predicted (in-sample) values for an estimated VAR model.

Usage

## S3 method for class 'bayesianVARs_bvar'
fitted(object, error_term = TRUE, ...)
## S3 method for class 'bayesianVARs_bvar'
fitted(object, error_term = TRUE, ...)

Arguments

object

A bayesianVARs_bvar object estimated via bvar().

error_term

logical indicating whether to include the error term or not.

...

Currently ignored.

Value

An object of class bayesianVARs_fitted.

Examples

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

# Simulate predicted historical values including the error term.
pred <- fitted(mod, error_term = TRUE)

# Simulate fitted historical values not including the error term.
fit <- fitted(mod, error_term = FALSE)

# Visualize
plot(pred)
plot(fit)
# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

# Simulate predicted historical values including the error term.
pred <- fitted(mod, error_term = TRUE)

# Simulate fitted historical values not including the error term.
fit <- fitted(mod, error_term = FALSE)

# Visualize
plot(pred)
plot(fit)

Impulse response functions

Description

Effect of the structural (factor) shocks over time.

Usage

irf(
  x,
  ahead = 8,
  structural_restrictions = NULL,
  shocks = NULL,
  hairy = FALSE,
  ...
)
irf(
  x,
  ahead = 8,
  structural_restrictions = NULL,
  shocks = NULL,
  hairy = FALSE,
  ...
)

Arguments

x

An object of type bayesianVARs_bvar.

ahead

maximum number of time horizons.

structural_restrictions

an object generated by specify_structural_restrictions. If specified, the IRFs to the structural shocks identified by the restrictions given in this argument will be calculated. If not specified, the orthogonal IRFs based on the Cholesky decomposition will be calculated. Note that the orthogonal IRFs depend on the ordering of the variables and do not necessarily correspond to shocks with a meaningful interpretation.

shocks

an matrix with r rows, where r is the number of shocks (see 'Details'). Each column specifies a shock. Default: diag(r), will calculate the responses to all structural (or factor) shocks of one standard deviation.

hairy

set to TRUE in order to plot each path seperately. To show valid quantiles, an Bayes optimal order of the posterior samples will be calculated which can take a long time even for moderately many samples. Default: FALSE.

...

Following expert arguments can be specified:

solver:: "randomized" or "lp". If some columns have more than one sign restriction, "lp" might find a solution, even if "randomized" is unable to. However "lp" can produce artifically narrow confidence bands which do not properly reflect the uncertainty in the identification scheme. Default: "randomized"
randomized_max_rotations_per_sample:: if using the "randomized" solver, how many rotations are drawn for each sample of the reduced form parameters in x. Default: 2.

Details

If a factor model was used, then the number of shocks is equal to the number of factors.

If the Cholesky model was used, then the number of shocks is equal to the number of variables.

Value

Returns a bayesianVARs_irf object.

Author(s)

Stefan Haan [email protected]

References

Arias, J. and Rubio-Ramírez, J. and Waggoner, D. (2014). Inference Based on SVARs Identified with Sign and Zero Restrictions: Theory and Applications. FRB Atlanta Working Paper Series, doi:10.2139/ssrn.2580264.

Examples

train_data <- 100 * usmacro_growth[,c("GDPC1", "GDPCTPI", "GS1", "M2REAL", "CPIAUCSL")]
prior_sigma <- specify_prior_sigma(train_data, type="cholesky", cholesky_heteroscedastic=FALSE)
mod <- bvar(train_data, lags=5L, prior_sigma=prior_sigma, quiet=TRUE)

structural_restrictions <- specify_structural_restrictions(
 mod,
 restrictions_B0=rbind(
   c(1 ,NA,0 ,NA,NA),
   c(0 ,1 ,0 ,NA,NA),
   c(0 ,NA,1 ,NA,NA),
   c(0 ,0 ,NA,1 ,NA),
   c(0 ,0 ,0 ,0 ,1 )
 )
)
irf_structural <- irf(
 mod, ahead=8,
 structural_restrictions=structural_restrictions
)
plot(irf_structural)

train_data <- 100 * usmacro_growth[,c("GDPC1", "GDPCTPI", "GS1", "M2REAL", "CPIAUCSL")]
prior_sigma <- specify_prior_sigma(train_data, type="cholesky", cholesky_heteroscedastic=FALSE)
mod <- bvar(train_data, lags=5L, prior_sigma=prior_sigma, quiet=TRUE)

structural_restrictions <- specify_structural_restrictions(
 mod,
 restrictions_B0=rbind(
   c(1 ,NA,0 ,NA,NA),
   c(0 ,1 ,0 ,NA,NA),
   c(0 ,NA,1 ,NA,NA),
   c(0 ,0 ,NA,1 ,NA),
   c(0 ,0 ,0 ,0 ,1 )
 )
)
irf_structural <- irf(
 mod, ahead=8,
 structural_restrictions=structural_restrictions
)
plot(irf_structural)

Draw from generalized inverse Gaussian

Description

Vectorized version of rgig

Usage

my_gig(n, lambda, chi, psi)
my_gig(n, lambda, chi, psi)

Arguments

n

A single integer indicating the number of draws to generate.

lambda

vector of shape parameters.

chi

vector of shape/scale parameters. Must be nonnegative for positive lambdas and positive else.

psi

vector of shape/scale parameters. Must be nonnegative for negative lambdas and positive else.

Value

Matrix of dimension c(n,m), where m is the maximum length of lambda, psi and chi.

Examples

gigsamples <- my_gig(2, c(1,1), c(1,1), c(1,1))
gigsamples <- my_gig(2, c(1,1), c(1,1), c(1,1))

Pairwise visualization of out-of-sample posterior predictive densities.

Description

Pairwise visualization of out-of-sample posterior predictive densities.

Usage

## S3 method for class 'bayesianVARs_predict'
pairs(x, vars, ahead, ...)
## S3 method for class 'bayesianVARs_predict'
pairs(x, vars, ahead, ...)

Arguments

x

An object of class bayesianVARs_predict obtained via predict.bayesianVARs_bvar().

vars

Integer vector (or coercible to such) indicating which variables to plot.

ahead

Integer vector (or coercible to such) indicating which step ahead to plot. max(ahead) must be smaller equal to dim(x$predictions)[1].

...

Currently ignored!

Value

Returns x invisibly.

Note

Note that that bayesianVARs_predict can also be used withing plot.bayesianVARs_bvar().

Examples

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

# Simulate from posterior predictive
predictions <- predict(mod, ahead = 1:3)

# Visualize
pairs(predictions, vars = 1:3, ahead = 1:3)
# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

# Simulate from posterior predictive
predictions <- predict(mod, ahead = 1:3)

# Visualize
pairs(predictions, vars = 1:3, ahead = 1:3)

Plot method for bayesianVARs_bvar

Description

Visualization of in-sample fit. Can also be used to display prediction intervals of future values.

Usage

## S3 method for class 'bayesianVARs_bvar'
plot(
  x,
  predictions = NULL,
  quantiles = c(0.05, 0.5, 0.95),
  dates = NULL,
  n_col = 1,
  ...
)
## S3 method for class 'bayesianVARs_bvar'
plot(
  x,
  predictions = NULL,
  quantiles = c(0.05, 0.5, 0.95),
  dates = NULL,
  n_col = 1,
  ...
)

Arguments

x

An object of class bayesianVARs_bvar obtained via bvar().

predictions

Optional array of out of sample predictions, e.g. obtained via predict.bayesianVARs_bvar().

quantiles

numeric vector indicating which quantiles to plot.

dates

optional vector of dates for labelling the x-axis. The default values is NULL; in this case, the axis will be labeled with numbers.

n_col

integer indicating the number of columns to use for plotting.

...

Currently ignored!

Value

Returns x invisibly.

Examples

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

# Simulate from posterior predictive
predictions <- predict(mod, ahead = 1:3)

# Visualize
plot(mod, predictions = predictions)
# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

# Simulate from posterior predictive
predictions <- predict(mod, ahead = 1:3)

# Visualize
plot(mod, predictions = predictions)

Visualization of in-sample fit of an estimated VAR.

Description

Visualization of in-sample fit of an estimated VAR.

Usage

## S3 method for class 'bayesianVARs_fitted'
plot(
  x,
  dates = NULL,
  vars = "all",
  quantiles = c(0.05, 0.5, 0.95),
  n_col = 1L,
  ...
)
## S3 method for class 'bayesianVARs_fitted'
plot(
  x,
  dates = NULL,
  vars = "all",
  quantiles = c(0.05, 0.5, 0.95),
  n_col = 1L,
  ...
)

Arguments

x

A bayesianVARs_fitted object.

dates

optional vector of dates for labelling the x-axis. The default values is NULL; in this case, the axis will be labeled with numbers.

vars

character vector containing the names of the variables to be visualized. The default is "all" indicating that the fit of all variables is visualized.

quantiles

numeric vector indicating which quantiles to plot.

n_col

integer indicating the number of columns to use for plotting.

...

Currently ignored.

Value

returns x invisibly

Examples

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

# Simulate predicted historical values including the error term.
pred <- fitted(mod, error_term = TRUE)

# Visualize
plot(pred)
# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

# Simulate predicted historical values including the error term.
pred <- fitted(mod, error_term = TRUE)

# Visualize
plot(pred)

Impulse Responses Plot

Description

Visualization of the impulse responses. Responses are plotted on a grid, where rows correspond to variables and columns correspond to shocks.

Usage

## S3 method for class 'bayesianVARs_irf'
plot(
  x,
  vars = "all",
  quantiles = c(0.05, 0.25, 0.5, 0.75, 0.95),
  default_hair_color = "#FF000003",
  true_irf = NULL,
  ...
)
## S3 method for class 'bayesianVARs_irf'
plot(
  x,
  vars = "all",
  quantiles = c(0.05, 0.25, 0.5, 0.75, 0.95),
  default_hair_color = "#FF000003",
  true_irf = NULL,
  ...
)

Arguments

x

An object of type bayesianVARs_irf obtained via irf.

vars

character vector containing the names of the variables to be visualized. The default is "all" indicating that all variables are visualized.

quantiles

numeric vector indicating which quantiles to plot. If hairy=TRUE was specified when calling irf, a proportion of max(quantiles) IRFs will be plotted. Specify 0 to plot a point-estimate only. If hairy=FALSE was specified (the default), point-wise quantiles will be plotted. Note that the curve of point-wise medians is not necessarily in the set of IRFs (see Inoue 2022).

default_hair_color

the color of the IRF samples, if hairy=TRUE was specified.

true_irf

If the true IRFs are known (because the data was simulated) they can be plotted alongside the estimates, such that the quality of the estimates may be judged. true_irf should be a numeric array with dimensions variables, shocks and time, in that order.

...

Currently ignored!

Author(s)

Stefan Haan [email protected]

References

Inoue, A. and Kilian, L. (2022). Joint Bayesian inference about impulse responses in VAR models. Journal of Econometrics, doi:10.1016/j.jeconom.2021.05.010.

Fan chart

Description

Visualization of (out-of-sample) predictive distribution.

Usage

## S3 method for class 'bayesianVARs_predict'
plot(
  x,
  dates = NULL,
  vars = "all",
  ahead = NULL,
  quantiles = c(0.05, 0.25, 0.5, 0.75, 0.95),
  n_col = 1L,
  first_obs = 1L,
  ...
)
## S3 method for class 'bayesianVARs_predict'
plot(
  x,
  dates = NULL,
  vars = "all",
  ahead = NULL,
  quantiles = c(0.05, 0.25, 0.5, 0.75, 0.95),
  n_col = 1L,
  first_obs = 1L,
  ...
)

Arguments

x

An object of type bayesianVARs_predict obtained via predict.bayesianVARs_bvar().

dates

optional vector of dates for labeling the x-axis. The default values is NULL; in this case, the axis will be labeled with numbers.

vars

character vector containing the names of the variables to be visualized. The default is "all" indicating that all variables are visualized.

ahead

Integer vector (or coercible to such) indicating which step ahead to plot. max(ahead) must be smaller equal to dim(x$predictions)[1].

quantiles

numeric vector indicating which quantiles to plot.

n_col

integer indicating the number of columns to use for plotting.

first_obs

integer indicating the first observation to be used for plotting.

...

Currently ignored!

Value

Returns x invisibly!

Examples

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

# Simulate from posterior predictive
predictions <- predict(mod, ahead = 1:3)

# Visualize
plot(predictions, vars = 1:3, ahead = 1:3)
# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

# Simulate from posterior predictive
predictions <- predict(mod, ahead = 1:3)

# Visualize
plot(predictions, vars = 1:3, ahead = 1:3)

Visualization of the residuals of an estimated VAR.

Description

Visualization of the residuals of an estimated VAR.

Usage

## S3 method for class 'bayesianVARs_residuals'
plot(
  x,
  dates = NULL,
  vars = "all",
  quantiles = c(0.05, 0.5, 0.95),
  n_col = 1L,
  ...
)
## S3 method for class 'bayesianVARs_residuals'
plot(
  x,
  dates = NULL,
  vars = "all",
  quantiles = c(0.05, 0.5, 0.95),
  n_col = 1L,
  ...
)

Arguments

x

A bayesianVARs_residuals object.

dates

optional vector of dates for labelling the x-axis. The default values is NULL; in this case, the axis will be labeled with numbers.

vars

character vector containing the names of the variables to be visualized. The default is "all" indicating that the fit of all variables is visualized.

quantiles

numeric vector indicating which quantiles to plot.

n_col

integer indicating the number of columns to use for plotting.

...

Currently ignored.

Value

returns x invisibly

Examples

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

mod.resids <- residuals(mod)

# Visualize
plot(mod.resids)
# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

mod.resids <- residuals(mod)

# Visualize
plot(mod.resids)

Posterior heatmaps for matrix valued parameters

Description

posterior_heatmap() generates a heatmap for draws of matrix values parameters visualizing point wise summaries, such as mean, median, variance, standard deviation, interquartile range etc. etc.

Usage

posterior_heatmap(
  x,
  FUN,
  ...,
  transpose = FALSE,
  colorbar = TRUE,
  colorbar_width = 0.1,
  gap_width = 0.25,
  xlabels = NULL,
  ylabels = NULL,
  add_numbers = FALSE,
  zlim = NULL,
  colspace = NULL,
  border_color = NA,
  zero_color = NA,
  main = "",
  detect_lags = TRUE,
  cex.axis = 0.75,
  cex.colbar = 1,
  cex.numbers = 1,
  asp = NULL
)
posterior_heatmap(
  x,
  FUN,
  ...,
  transpose = FALSE,
  colorbar = TRUE,
  colorbar_width = 0.1,
  gap_width = 0.25,
  xlabels = NULL,
  ylabels = NULL,
  add_numbers = FALSE,
  zlim = NULL,
  colspace = NULL,
  border_color = NA,
  zero_color = NA,
  main = "",
  detect_lags = TRUE,
  cex.axis = 0.75,
  cex.colbar = 1,
  cex.numbers = 1,
  asp = NULL
)

Arguments

x

An array of dimension $a \times b \times draws$ , where $a \times b$ is the dimension of the parameter to visualize and draws is the number of posterior draws.

FUN

The summary function to be applied to margins c(1,2) of x. E.g. "median", "mean", "IQR", "sd" or "var". apply(x, 1:2, FUN, ...) must return a matrix!

...

optional arguments to FUN.

transpose

logical indicating whether to transpose the matrix or not, i.e. whether to plot an $a \times b$ or an $b \times a$ matrix. Default is FALSE.

colorbar

logical indicating whether to display a colorbar or not. Default is TRUE.

colorbar_width

numeric. A value between 0 and 1 indicating the proportion of the width of the plot for the colorbar.

gap_width

numeric. A value between 0 and 1 indicating the width of the gap between the heatmap and the colorbar. The width is computed as gap_width*colorbar_width.

xlabels

ylabels=NULL, the default, indicates that colnames(x) will be displayed. ylabels="" indicates that no ylabels will be displayed.

ylabels

xlabels=NULL, the default, indicates that rownames(x) will be displayed. xlabels="" indicates that no ylabels are displayed.

add_numbers

logical. add_numbers=TRUE, the default indicates that the actual values of summary will be displayed.

zlim

numeric vector of length two indicating the minimum and maximum values for which colors should be plotted. By default this range is determined by the maximum of the absolute values of the selected summary.

colspace

Optional argument indicating the color palette to be used. If not specified, colorspace::diverge_hcl() will be used, or, if FUN returns only positive values colorspace::sequential_hcl(). See below for a more detailed description of the default usage.

border_color

The color of the rectangles borders. If not specified no borders will be displayed.

zero_color

The color of exact zero elements. By default this is not specified and then will depend on colspace.

main

main title for the plot.

detect_lags

logical. If class(x) is "bayesianVARs_coef", then detect_lags=TRUE will separate the sub matrices corresponding to the lags with black lines.

cex.axis

The magnification to be used for y-axis annotation relative to the current setting of cex.

cex.colbar

The magnification to be used for colorbar annotation relative to the current setting of cex.

cex.numbers

The magnification to be used for the actual values (if add_numbers=TRUE) relative to the current setting of cex.

asp

aspect ratio.

Details

colspace

If not specified either colorspace::diverge_hcl(1001, alpha = alpha, palette = "Blue-Red") or colorspace::sequential_hcl(1001, alpha = alpha, rev = TRUE, palette = "Reds 2") will be used.

Value

Returns x invisibly.

Examples

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate a model
mod <- bvar(100*data, sv_keep = "all", quiet = TRUE)

# Extract posterior draws of VAR coefficients
phi_post <- coef(mod)

# Visualize posterior median of VAR coefficients
posterior_heatmap(phi_post, median, detect_lags = TRUE, border_color = rgb(0, 0, 0, alpha = 0.2))

# Extract posterior draws of variance-covariance matrices (for each point in time)
sigma_post <- vcov(mod)
# Visualize posterior interquartile-range of variance-covariance matrix of the first observation
posterior_heatmap(sigma_post[1,,,], IQR)
# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate a model
mod <- bvar(100*data, sv_keep = "all", quiet = TRUE)

# Extract posterior draws of VAR coefficients
phi_post <- coef(mod)

# Visualize posterior median of VAR coefficients
posterior_heatmap(phi_post, median, detect_lags = TRUE, border_color = rgb(0, 0, 0, alpha = 0.2))

# Extract posterior draws of variance-covariance matrices (for each point in time)
sigma_post <- vcov(mod)
# Visualize posterior interquartile-range of variance-covariance matrix of the first observation
posterior_heatmap(sigma_post[1,,,], IQR)

Predict method for Bayesian VARs

Description

Simulates from (out-of-sample) predictive density for Bayesian VARs estimated via bvar() and computes log predictive likelhoods if ex-post observed data is supplied.

Usage

## S3 method for class 'bayesianVARs_bvar'
predict(
  object,
  ahead = 1L,
  each = 1L,
  stable = TRUE,
  simulate_predictive = TRUE,
  LPL = FALSE,
  Y_obs = NA,
  LPL_VoI = NA,
  ...
)
## S3 method for class 'bayesianVARs_bvar'
predict(
  object,
  ahead = 1L,
  each = 1L,
  stable = TRUE,
  simulate_predictive = TRUE,
  LPL = FALSE,
  Y_obs = NA,
  LPL_VoI = NA,
  ...
)

Arguments

object

A bayesianVARs_bvar object, obtained from bvar().

ahead

Integer vector (or coercible to such), indicating the number of steps ahead at which to predict.

each

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

stable

logical indicating whether to consider only those draws from the posterior that fulfill the 'stable' criterion. Default is TRUE.

simulate_predictive

logical, indicating whether the posterior predictive distribution should be simulated.

LPL

logical indicating whether ahead-step-ahead log predictive likelihoods should be computed. If LPL=TRUE, Y_obs has to be specified.

Y_obs

Data matrix of observed values for computation of log predictive likelihood. Each of ncol(object$Yraw) columns is assumed to contain a single time-series of length length(ahead).

LPL_VoI

either integer vector or character vector of column-names indicating for which subgroup of time-series in object$Yraw a joint log predictive likelihood shall be computed.

...

Currently ignored!

Value

Object of class bayesianVARs_predict, a list that may contain the following elements:

predictions array of dimensions c(length(ahead), ncol(object$Yraw), each * dim(object$PHI)[3]) containing the simulations from the predictive density (if simulate_predictive=TRUE).
LPL vector of length length(ahead) containing the log-predictive-likelihoods (taking into account the joint distribution of all variables) (if LPL=TRUE).
LPL_univariate matrix of dimension ⁠c(length(ahead), ncol(object$Yraw)⁠ containing the marginalized univariate log-predictive-likelihoods of each series (if LPL=TRUE).
LPL_VoI vector of length length(ahead) containing the log-predictive-likelihoods for a subset of variables (if LPL=TRUE and LPL_VoI != NA).
Yraw matrix containing the data used for the estimation of the VAR.
LPL_draws matrix containing the simulations of the log-predictive-likelihood (if LPL=TRUE).
PL_univariate_draws array containing the simulations of the univariate predictive-likelihoods (if LPL=TRUE).
LPL_sub_draws matrix containing the simulations of the log-predictive-likelihood for a subset of variables (if LPL=TRUE and LPL_VoI != NA).

Examples

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Split data in train and test
train <- data[1:(nrow(data)-4),]
test <- data[-c(1:(nrow(data)-4)),]

# Estimate model using train data only
mod <- bvar(train, quiet = TRUE)

# Simulate from 1-step to 4-steps ahead posterior predictive and compute
# log-predictive-likelihoods
predictions <- predict(mod, ahead = 1:4, LPL = TRUE, Y_obs = test)

# Summary
summary(predictions)

# Visualize via fan-charts
plot(predictions)


# In order to evaluate the joint predictive density of a subset of the
# variables (variables of interest), consider specifying 'LPL_VoI':
predictions <- predict(mod, ahead = 1:4, LPL = TRUE, Y_obs = test, LPL_VoI = c("GDPC1","FEDFUNDS"))
predictions$LPL_VoI

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Split data in train and test
train <- data[1:(nrow(data)-4),]
test <- data[-c(1:(nrow(data)-4)),]

# Estimate model using train data only
mod <- bvar(train, quiet = TRUE)

# Simulate from 1-step to 4-steps ahead posterior predictive and compute
# log-predictive-likelihoods
predictions <- predict(mod, ahead = 1:4, LPL = TRUE, Y_obs = test)

# Summary
summary(predictions)

# Visualize via fan-charts
plot(predictions)


# In order to evaluate the joint predictive density of a subset of the
# variables (variables of interest), consider specifying 'LPL_VoI':
predictions <- predict(mod, ahead = 1:4, LPL = TRUE, Y_obs = test, LPL_VoI = c("GDPC1","FEDFUNDS"))
predictions$LPL_VoI

Pretty printing of a bvar object

Description

Pretty printing of a bvar object

Usage

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

Arguments

x

Object of class bayesianVARs_bvar, usually resulting from a call of bvar().

...

Ignored.

Value

Returns x invisibly.

Examples

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

# Print model
mod

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

# Print model
mod

Print method for bayesianVARs_predict objects

Description

Print method for bayesianVARs_predict objects.

Usage

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

Arguments

x

A bayesianVARs_predict object obtained via predict.bayesianVARs_bvar().

...

Currently ignored!

Value

Returns x invisibly.

Examples

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Split data in train and test
train <- data[1:(nrow(data)-4),]
test <- data[-c(1:(nrow(data)-4)),]

# Estimate model using train data only
mod <- bvar(train, quiet = TRUE)

# Simulate from 1-step ahead posterior predictive
predictions <- predict(mod, ahead = 1L)
print(predictions)
# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Split data in train and test
train <- data[1:(nrow(data)-4),]
test <- data[-c(1:(nrow(data)-4)),]

# Estimate model using train data only
mod <- bvar(train, quiet = TRUE)

# Simulate from 1-step ahead posterior predictive
predictions <- predict(mod, ahead = 1L)
print(predictions)

Print method for summary.bayesianVARs_bvar objects

Description

Print method for summary.bayesianVARs_bvar objects.

Usage

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

Arguments

x

A summary.bayesianVARs_bvar object obtained via summary.bayesianVARs_bvar().

...

Currently ignored!

Value

Returns x invisibly!

Examples

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate model
mod <- bvar(data, quiet = TRUE)

# Print summary
summary(mod)
# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate model
mod <- bvar(data, quiet = TRUE)

# Print summary
summary(mod)

Print method for summary.bayesianVARs_predict objects

Description

Print method for summary.bayesianVARs_predict objects.

Usage

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

Arguments

x

A summary.bayesianVARs_predict object obtained via summary.bayesianVARs_predict().

...

Currently ignored.

Value

Returns x invisibly.

Examples

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Split data in train and test
train <- data[1:(nrow(data)-4),]
test <- data[-c(1:(nrow(data)-4)),]

# Estimate model using train data only
mod <- bvar(train, quiet = TRUE)

# Simulate from 1-step ahead posterior predictive
predictions <- predict(mod, ahead = 1L)
sum <- summary(predictions)
print(sum)

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Split data in train and test
train <- data[1:(nrow(data)-4),]
test <- data[-c(1:(nrow(data)-4)),]

# Estimate model using train data only
mod <- bvar(train, quiet = TRUE)

# Simulate from 1-step ahead posterior predictive
predictions <- predict(mod, ahead = 1L)
sum <- summary(predictions)
print(sum)

Extract Model Residuals

Description

Extract model residuals, defined as the difference between the observed time-series and the in-sample predictions of the VAR model. Because in-sample prediction is subject to uncertainty of the VAR parameter estimates, this uncertainty carries over to the model residuals.

Usage

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

Arguments

object

A bayesianVARs_bvar object estimated via bvar().

...

Passed to fitted.bayesianVARs_bvar().

Value

An object of class bayesianVARs_residuals.

Examples

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

mod.resids <- residuals(mod)
plot(mod.resids)
# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

mod.resids <- residuals(mod)
plot(mod.resids)

Specify prior on PHI

Description

Configures prior on PHI, the matrix of reduced-form VAR coefficients.

Usage

specify_prior_phi(
  data = NULL,
  M = ncol(data),
  lags = 1L,
  prior = "HS",
  priormean = 0,
  PHI_tol = 1e-18,
  DL_a = "1/K",
  DL_tol = 0,
  R2D2_a = 0.1,
  R2D2_b = 0.5,
  R2D2_tol = 0,
  NG_a = 0.1,
  NG_b = 1,
  NG_c = 1,
  NG_tol = 0,
  SSVS_c0 = 0.01,
  SSVS_c1 = 100,
  SSVS_semiautomatic = TRUE,
  SSVS_p = 0.5,
  HMP_lambda1 = c(0.01, 0.01),
  HMP_lambda2 = c(0.01, 0.01),
  normal_sds = 10,
  global_grouping = "global",
  ...
)
specify_prior_phi(
  data = NULL,
  M = ncol(data),
  lags = 1L,
  prior = "HS",
  priormean = 0,
  PHI_tol = 1e-18,
  DL_a = "1/K",
  DL_tol = 0,
  R2D2_a = 0.1,
  R2D2_b = 0.5,
  R2D2_tol = 0,
  NG_a = 0.1,
  NG_b = 1,
  NG_c = 1,
  NG_tol = 0,
  SSVS_c0 = 0.01,
  SSVS_c1 = 100,
  SSVS_semiautomatic = TRUE,
  SSVS_p = 0.5,
  HMP_lambda1 = c(0.01, 0.01),
  HMP_lambda2 = c(0.01, 0.01),
  normal_sds = 10,
  global_grouping = "global",
  ...
)

Arguments

data

Optional. Data matrix (can be a time series object). Each of $M$ columns is assumed to contain a single time-series of length $T$ .

M

positive integer indicating the number of time-series of the VAR.

lags

positive integer indicating the order of the VAR, i.e. the number of lags of the dependent variables included as predictors.

prior

character, one of "HS", "R2D2", "NG", "DL", "SSVS", "HMP" or "normal".

priormean

real numbers indicating the prior means of the VAR coefficients. One single number means that the prior mean of all own-lag coefficients w.r.t. the first lag equals priormean and 0 else. A vector of length M means that the prior mean of the own-lag coefficients w.r.t. the first lag equals priormean and 0 else. If priormean is a matrix of dimension c(lags*M,M), then each of the $M$ columns is assumed to contain lags*M prior means for the VAR coefficients of the respective VAR equations.

PHI_tol

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

DL_a

(Single) positive real number. The value is interpreted as the concentration parameter for the local scales. Smaller values enforce heavier shrinkage. If the argument global_grouping specifies e.g. k groups, then DL_a can be a numeric vector of length k and the elements indicate the shrinkage in each group. A matrix of dimension c(s,2) specifies a discrete hyperprior, where the first column contains s support points and the second column contains the associated prior probabilities. DL_a has only to be specified if prior="DL".

DL_tol

Minimum number that a parameter draw of one of the shrinking parameters of the Dirichlet Laplace prior can take. Prevents numerical issues that can appear when strong shrinkage is enforced if chosen to be greater than zero. DL_tol has only to be specified if prior="DL".

R2D2_a

(Single) positive real number. The value is interpreted as the concentration parameter for the local scales. Smaller values enforce heavier shrinkage. If the argument global_grouping specifies e.g. k groups, then R2D2_a can be a numeric vector of length k and the elements indicate the shrinkage in each group. A matrix of dimension c(s,2) specifies a discrete hyperprior, where the first column contains s support points and the second column contains the associated prior probabilities. R2D2_a has only to be specified if prior="R2D2".

R2D2_b

(Single) positive real number. The value indicates the shape parameter of the inverse gamma prior on the (semi-)global scales. If the argument global_grouping specifies e.g. k groups, then NG_b can be a numeric vector of length k and the elements determine the shape parameter in each group. R2D2_b has only to be specified if prior="R2D2".

R2D2_tol

Minimum number that a parameter draw of one of the shrinking parameters of the R2D2 prior can take. Prevents numerical issues that can appear when strong shrinkage is enforced if chosen to be greater than zero. R2D2_tol has only to be specified if prior="R2D2".

NG_a

(Single) positive real number. The value is interpreted as the concentration parameter for the local scales. Smaller values enforce heavier shrinkage. If the argument global_grouping specifies e.g. k groups, then NG_a can be a numeric vector of length k and the elements indicate the shrinkage in each group. A matrix of dimension c(s,2) specifies a discrete hyperprior, where the first column contains s support points and the second column contains the associated prior probabilities. NG_a has only to be specified if prior="NG".

NG_b

NG_c

(Single) positive real number. The value indicates the scale parameter of the inverse gamma prior on the (semi-)global scales. If the argument global_grouping specifies e.g. k groups, then NG_c can be a numeric vector of length k and the elements determine the scale parameter in each group. Expert option would be to set the scale parameter proportional to NG_a. E.g. in the case where a discrete hyperprior for NG_a is chosen, a desired proportion of let's say 0.2 is achieved by setting NG_c="0.2*a" (character input!). NG_c has only to be specified if prior="NG".

NG_tol

Minimum number that a parameter draw of one of the shrinking parameters of the normal-gamma prior can take. Prevents numerical issues that can appear when strong shrinkage is enforced if chosen to be greater than zero. NG_tol has only to be specified if prior="NG".

SSVS_c0

single positive number indicating the (unscaled) standard deviation of the spike component. SSVS_c0 has only to be specified if prior="SSVS". It should be that $SSVS_{c0} \ll SSVS_{c1}$ ! SSVS_c0 has only to be specified if prior="SSVS".

SSVS_c1

single positive number indicating the (unscaled) standard deviation of the slab component. SSVS_c0 has only to be specified if prior="SSVS". It should be that $SSVS_{c0} \ll SSVS_{c1}$ !

SSVS_semiautomatic

logical. If SSVS_semiautomatic=TRUE both SSVS_c0 and SSVS_c1 will be scaled by the variances of the posterior of PHI under a FLAT conjugate (dependent Normal-Wishart prior). SSVS_semiautomatic has only to be specified if prior="SSVS".

SSVS_p

Either a single positive number in the range (0,1) indicating the (fixed) prior inclusion probability of each coefficient. Or numeric vector of length 2 with positive entries indicating the shape parameters of the Beta distribution. In that case a Beta hyperprior is placed on the prior inclusion probability. SSVS_p has only to be specified if prior="SSVS".

HMP_lambda1

numeric vector of length 2. Both entries must be positive. The first indicates the shape and the second the rate of the Gamma hyperprior on own-lag coefficients. HMP_lambda1 has only to be specified if prior="HMP".

HMP_lambda2

numeric vector of length 2. Both entries must be positive. The first indicates the shape and the second the rate of the Gamma hyperprior on cross-lag coefficients. HMP_lambda2 has only to be specified if prior="HMP".

normal_sds

numeric vector of length $n$ , where $n = lags M^2$ is the number of all VAR coefficients (excluding the intercept), indicating the prior variances. A single number will be recycled accordingly! Must be positive. normal_sds has only to be specified if prior="normal".

global_grouping

One of "global", "equation-wise", "covariate-wise", "olcl-lagwise" "fol" indicating the sub-groups of the semi-global(-local) modifications to HS, R2D2, NG, DL and SSVS prior. Works also with user-specified indicator matrix of dimension c(lags*M,M). Only relevant if prior="HS", prior="DL", prior="R2D2", prior="NG" or prior="SSVS".

...

Do not use!

Details

For details concerning prior-elicitation for VARs please see Gruber & Kastner (2025).

Currently one can choose between six hierarchical shrinkage priors and a normal prior: prior="HS" stands for the Horseshoe-prior, ⁠prior="R2D2⁠ for the R $^2$ -induced-Dirichlet-decompostion-prior, prior="NG" for the normal-gamma-prior, prior="DL" for the Dirichlet-Laplace-prior, prior="SSVS" for the stochastic-search-variable-selection-prior, prior="HMP" for the semi-hierarchical Minnesota prior and prior=normal for the normal-prior.

Semi-global shrinkage, i.e. group-specific shrinkage for pre-specified subgroups of the coefficients, can be achieved through the argument global_grouping.

Value

A baysianVARs_prior_phi-object.

References

Gruber, L. and Kastner, G. (2025). Forecasting macroeconomic data with Bayesian VARs: Sparse or dense? It depends! International Journal of Forecasting. doi:10.1016/j.ijforecast.2025.02.001.

Examples

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Horseshoe prior for a VAR(2)
phi_hs <- specify_prior_phi(data = data, lags = 2L ,prior = "HS")

# Semi-global-local Horseshoe prior for a VAR(2) with semi-global shrinkage parameters for
# cross-lag and own-lag coefficients in each lag
phi_hs_sg <- specify_prior_phi(data = data, lags = 2L, prior = "HS",
global_grouping = "olcl-lagwise")

# Semi-global-local Horseshoe prior for a VAR(2) with equation-wise shrinkage
# construct indicator matrix for equation-wise shrinkage
semi_global_mat <- matrix(1:ncol(data), 2*ncol(data), ncol(data),
byrow = TRUE)
phi_hs_ew <- specify_prior_phi(data = data, lags = 2L, prior = "HS",
global_grouping = semi_global_mat)
# (for equation-wise shrinkage one can also use 'global_grouping = "equation-wise"')


# Estimate model with your prior configuration of choice
mod <- bvar(data, lags = 2L, prior_phi = phi_hs_sg, quiet = TRUE)

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Horseshoe prior for a VAR(2)
phi_hs <- specify_prior_phi(data = data, lags = 2L ,prior = "HS")

# Semi-global-local Horseshoe prior for a VAR(2) with semi-global shrinkage parameters for
# cross-lag and own-lag coefficients in each lag
phi_hs_sg <- specify_prior_phi(data = data, lags = 2L, prior = "HS",
global_grouping = "olcl-lagwise")

# Semi-global-local Horseshoe prior for a VAR(2) with equation-wise shrinkage
# construct indicator matrix for equation-wise shrinkage
semi_global_mat <- matrix(1:ncol(data), 2*ncol(data), ncol(data),
byrow = TRUE)
phi_hs_ew <- specify_prior_phi(data = data, lags = 2L, prior = "HS",
global_grouping = semi_global_mat)
# (for equation-wise shrinkage one can also use 'global_grouping = "equation-wise"')


# Estimate model with your prior configuration of choice
mod <- bvar(data, lags = 2L, prior_phi = phi_hs_sg, quiet = TRUE)

Specify prior on Sigma

Description

Configures prior on the variance-covariance of the VAR.

Usage

specify_prior_sigma(
  data = NULL,
  M = ncol(data),
  type = c("factor", "cholesky"),
  factor_factors = 1L,
  factor_restrict = c("none", "upper"),
  factor_priorfacloadtype = c("rowwiseng", "colwiseng", "normal"),
  factor_priorfacload = 0.1,
  factor_facloadtol = 1e-14,
  factor_priorng = c(1, 1),
  factor_priormu = c(0, 10),
  factor_priorphiidi = c(10, 3),
  factor_priorphifac = c(10, 3),
  factor_priorsigmaidi = 1,
  factor_priorsigmafac = 1,
  factor_priorh0idi = "stationary",
  factor_priorh0fac = "stationary",
  factor_heteroskedastic = TRUE,
  factor_priorhomoskedastic = NA,
  factor_interweaving = 4,
  cholesky_U_prior = c("HS", "DL", "R2D2", "NG", "SSVS", "normal", "HMP"),
  cholesky_U_tol = 1e-18,
  cholesky_heteroscedastic = TRUE,
  cholesky_priormu = c(0, 100),
  cholesky_priorphi = c(20, 1.5),
  cholesky_priorsigma2 = c(0.5, 0.5),
  cholesky_priorh0 = "stationary",
  cholesky_priorhomoscedastic = as.numeric(NA),
  cholesky_DL_a = "1/n",
  cholesky_DL_tol = 0,
  cholesky_R2D2_a = 0.4,
  cholesky_R2D2_b = 0.5,
  cholesky_R2D2_tol = 0,
  cholesky_NG_a = 0.5,
  cholesky_NG_b = 0.5,
  cholesky_NG_c = 0.5,
  cholesky_NG_tol = 0,
  cholesky_SSVS_c0 = 0.001,
  cholesky_SSVS_c1 = 1,
  cholesky_SSVS_p = 0.5,
  cholesky_HMP_lambda3 = c(0.01, 0.01),
  cholesky_normal_sds = 10,
  expert_sv_offset = 0,
  quiet = FALSE,
  ...
)
specify_prior_sigma(
  data = NULL,
  M = ncol(data),
  type = c("factor", "cholesky"),
  factor_factors = 1L,
  factor_restrict = c("none", "upper"),
  factor_priorfacloadtype = c("rowwiseng", "colwiseng", "normal"),
  factor_priorfacload = 0.1,
  factor_facloadtol = 1e-14,
  factor_priorng = c(1, 1),
  factor_priormu = c(0, 10),
  factor_priorphiidi = c(10, 3),
  factor_priorphifac = c(10, 3),
  factor_priorsigmaidi = 1,
  factor_priorsigmafac = 1,
  factor_priorh0idi = "stationary",
  factor_priorh0fac = "stationary",
  factor_heteroskedastic = TRUE,
  factor_priorhomoskedastic = NA,
  factor_interweaving = 4,
  cholesky_U_prior = c("HS", "DL", "R2D2", "NG", "SSVS", "normal", "HMP"),
  cholesky_U_tol = 1e-18,
  cholesky_heteroscedastic = TRUE,
  cholesky_priormu = c(0, 100),
  cholesky_priorphi = c(20, 1.5),
  cholesky_priorsigma2 = c(0.5, 0.5),
  cholesky_priorh0 = "stationary",
  cholesky_priorhomoscedastic = as.numeric(NA),
  cholesky_DL_a = "1/n",
  cholesky_DL_tol = 0,
  cholesky_R2D2_a = 0.4,
  cholesky_R2D2_b = 0.5,
  cholesky_R2D2_tol = 0,
  cholesky_NG_a = 0.5,
  cholesky_NG_b = 0.5,
  cholesky_NG_c = 0.5,
  cholesky_NG_tol = 0,
  cholesky_SSVS_c0 = 0.001,
  cholesky_SSVS_c1 = 1,
  cholesky_SSVS_p = 0.5,
  cholesky_HMP_lambda3 = c(0.01, 0.01),
  cholesky_normal_sds = 10,
  expert_sv_offset = 0,
  quiet = FALSE,
  ...
)

Arguments

data

Optional. Data matrix (can be a time series object). Each of $M$ columns is assumed to contain a single time-series of length $T$ .

M

positive integer indicating the number of time-series of the VAR.

type

character, one of "factor" (the default) or "cholesky", indicating which decomposition to be applied to the covariance-matrix.

factor_factors

Number of latent factors to be estimated. Only required if type="factor".

factor_restrict

Either "upper" or "none", indicating whether the factor loadings matrix should be restricted to have zeros above the diagonal ("upper") or whether all elements should be estimated from the data ("none"). Setting restrict to "upper" often stabilizes MCMC estimation and can be important for identifying the factor loadings matrix, however, it generally is a strong prior assumption. Setting restrict to "none" is usually the preferred option if identification of the factor loadings matrix is of less concern but covariance estimation or prediction is the goal. Only required if type="factor".

factor_priorfacloadtype

Can be "normal", "rowwiseng", "colwiseng". Only required if type="factor".

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

For details please see Kastner (2019).

factor_priorfacload

Either a matrix of dimensions M times factor_factors with positive elements or a single number (which will be recycled accordingly). Only required if type="factor". The meaning of factor_priorfacload depends on the setting of factor_priorfacloadtype and is explained there.

factor_facloadtol

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

factor_priorng

Two-element vector with positive entries indicating the Normal-Gamma prior's hyperhyperparameters c and d (cf. Kastner (2019)). Only required if type="factor".

factor_priormu

Vector of length 2 denoting prior mean and standard deviation for unconditional levels of the idiosyncratic log variance processes. Only required if type="factor".

factor_priorphiidi

Vector of length 2, indicating the shape parameters for the Beta prior distributions of the transformed parameters (phi+1)/2, where phi denotes the persistence of the idiosyncratic log variances. Only required if type="factor".

factor_priorphifac

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

factor_priorsigmaidi

Vector of length M containing the prior volatilities of log variances. If factor_priorsigmaidi has exactly one element, it will be recycled for all idiosyncratic log variances. Only required if type="factor".

factor_priorsigmafac

Vector of length factor_factors containing the prior volatilities of log variances. If factor_priorsigmafac has exactly one element, it will be recycled for all factor log variances. Only required if type="factor".

factor_priorh0idi

Vector of length 1 or M, containing information about the Gaussian prior for the initial idiosyncratic log variances. Only required if type="factor". If an element of factor_priorh0idi is a nonnegative number, the conditional prior of the corresponding initial log variance h0 is assumed to be Gaussian with mean 0 and standard deviation factor_priorh0idi times $sigma$ . If an element of factor_priorh0idi is the string 'stationary', the prior of the corresponding initial log volatility is taken to be from the stationary distribution, i.e. h0 is assumed to be Gaussian with mean 0 and variance $sigma^2/(1-phi^2)$ .

factor_priorh0fac

Vector of length 1 or factor_factors, containing information about the Gaussian prior for the initial factor log variances. Only required if type="factor". If an element of factor_priorh0fac is a nonnegative number, the conditional prior of the corresponding initial log variance h0 is assumed to be Gaussian with mean 0 and standard deviation factor_priorh0fac times $sigma$ . If an element of factor_priorh0fac is the string 'stationary', the prior of the corresponding initial log volatility is taken to be from the stationary distribution, i.e. h0 is assumed to be Gaussian with mean 0 and variance $sigma^2/(1-phi^2)$ .

factor_heteroskedastic

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

factor_priorhomoskedastic

Only used if at least one element of factor_heteroskedastic is set to FALSE. In that case, factor_priorhomoskedastic must be a matrix with positive entries and dimension c(M, 2). Values in column 1 will be interpreted as the shape and values in column 2 will be interpreted as the rate parameter of the corresponding inverse gamma prior distribution of the idiosyncratic variances. Only required if type="factor".

factor_interweaving

The following values for interweaving the factor loadings are accepted (Only required if type="factor"):

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

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

cholesky_U_prior

character, one of "HS", "R2D2", "NG", "DL", "SSVS", "HMP" or "normal". Only required if type="cholesky".

cholesky_U_tol

Minimum number that the absolute value of an free off-diagonal element of an $U$ -draw can take. Prevents numerical issues that can appear when strong shrinkage is enforced if chosen to be greater than zero. Only required if type="cholesky".

cholesky_heteroscedastic

single logical indicating whether time-varying (cholesky_heteroscedastic = TRUE) or constant (cholesky_heteroscedastic = FALSE) variance should be estimated. Only required if type="cholesky".

cholesky_priormu

Vector of length 2 denoting prior mean and standard deviation for unconditional levels of the log variance processes. Only required if type="cholesky".

cholesky_priorphi

Vector of length 2, indicating the shape parameters for the Beta prior distributions of the transformed parameters (phi+1)/2, where phi denotes the persistence of the log variances. Only required if type="cholesky".

cholesky_priorsigma2

Vector of length 2, indicating the shape and the rate for the Gamma prior distributions on the variance of the log variance processes. (Currently only one global setting for all $M$ processes is supported). Only required if type="cholesky".

cholesky_priorh0

Vector of length 1 or M, containing information about the Gaussian prior for the initial idiosyncratic log variances. Only required if type="cholesky". If an element of cholesky_priorh0 is a nonnegative number, the conditional prior of the corresponding initial log variance h0 is assumed to be Gaussian with mean 0 and standard deviation cholesky_priorh0 times $sigma$ . If an element of cholesky_priorh0 is the string 'stationary', the prior of the corresponding initial log volatility is taken to be from the stationary distribution, i.e. h0 is assumed to be Gaussian with mean 0 and variance $sigma^2/(1-phi^2)$ .

cholesky_priorhomoscedastic

Only used if cholesky_heteroscedastic=FALSE. In that case, cholesky_priorhomoscedastic must be a matrix with positive entries and dimension c(M, 2). Values in column 1 will be interpreted as the shape and values in column 2 will be interpreted as the scale parameter of the corresponding inverse gamma prior distribution of the variances. Only required if type="cholesky".

cholesky_DL_a

(Single) positive real number. The value is interpreted as the concentration parameter for the local scales. Smaller values enforce heavier shrinkage. A matrix of dimension c(s,2) specifies a discrete hyperprior, where the first column contains s support points and the second column contains the associated prior probabilities. cholesky_DL_a has only to be specified if cholesky_U_prior="DL".

cholesky_DL_tol

cholesky_R2D2_a

(Single) positive real number. The value is interpreted as the concentration parameter for the local scales. Smaller values enforce heavier shrinkage. A matrix of dimension c(s,2) specifies a discrete hyperprior, where the first column contains s support points and the second column contains the associated prior probabilities. cholesky_R2D2_a has only to be specified if cholesky_U_prior="R2D2".

cholesky_R2D2_b

single positive number, where greater values indicate heavier regularization. cholesky_R2D2_b has only to be specified if cholesky_U_prior="R2D2".

cholesky_R2D2_tol

Minimum number that a parameter draw of one of the shrinking parameters of the R2D2 prior can take. Prevents numerical issues that can appear when strong shrinkage is enforced if chosen to be greater than zero. cholesky_R2D2_tol has only to be specified if cholesky_U_prior="R2D2".

cholesky_NG_a

(Single) positive real number. The value is interpreted as the concentration parameter for the local scales. Smaller values enforce heavier shrinkage. A matrix of dimension c(s,2) specifies a discrete hyperprior, where the first column contains s support points and the second column contains the associated prior probabilities. cholesky_NG_a has only to be specified if cholesky_U_prior="NG".

cholesky_NG_b

(Single) positive real number. The value indicates the shape parameter of the inverse gamma prior on the global scales. cholesky_NG_b has only to be specified if cholesky_U_prior="NG".

cholesky_NG_c

(Single) positive real number. The value indicates the scale parameter of the inverse gamma prior on the global scales. Expert option would be to set the scale parameter proportional to NG_a. E.g. in the case where a discrete hyperprior for NG_a is chosen, a desired proportion of let's say 0.2 is achieved by setting NG_c="0.2a" (character input!). cholesky_NG_c has only to be specified if cholesky_U_prior="NG".

cholesky_NG_tol

cholesky_SSVS_c0

single positive number indicating the (unscaled) standard deviation of the spike component. cholesky_SSVS_c0 has only to be specified if choleksy_U_prior="SSVS". It should be that $SSVS_{c0} \ll SSVS_{c1}$ !

cholesky_SSVS_c1

single positive number indicating the (unscaled) standard deviation of the slab component. cholesky_SSVS_c1 has only to be specified if choleksy_U_prior="SSVS". It should be that $SSVS_{c0} \ll SSVS_{c1}$ !

cholesky_SSVS_p

Either a single positive number in the range ⁠(0,1)⁠ indicating the (fixed) prior inclusion probability of each coefficient. Or numeric vector of length 2 with positive entries indicating the shape parameters of the Beta distribution. In that case a Beta hyperprior is placed on the prior inclusion probability. cholesky_SSVS_p has only to be specified if choleksy_U_prior="SSVS".

cholesky_HMP_lambda3

numeric vector of length 2. Both entries must be positive. The first indicates the shape and the second the rate of the Gamma hyperprior on the contemporaneous coefficients. cholesky_HMP_lambda3 has only to be specified if choleksy_U_prior="HMP".

cholesky_normal_sds

numeric vector of length $\frac{M^2-M}{2}$ , indicating the prior variances for the free off-diagonal elements in $U$ . A single number will be recycled accordingly! Must be positive. cholesky_normal_sds has only to be specified if choleksy_U_prior="normal".

expert_sv_offset

... Do not use!

quiet

logical indicating whether informative output should be omitted.

...

Do not use!

Details

Value

Object of class bayesianVARs_prior_sigma.

References

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

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

Examples

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# examples with stochastic volatility (heteroscedasticity) -----------------
# factor-decomposition with 2 factors and colwise normal-gamma prior on the loadings
sigma_factor_cng_sv <- specify_prior_sigma(data = data, type = "factor",
factor_factors = 2L, factor_priorfacloadtype = "colwiseng", factor_heteroskedastic = TRUE)

# cholesky-decomposition with Dirichlet-Laplace prior on U
sigma_cholesky_dl_sv <- specify_prior_sigma(data = data, type = "cholesky",
cholesky_U_prior = "DL", cholesky_DL_a = 0.5, cholesky_heteroscedastic = TRUE)

# examples without stochastic volatility (homoscedasticity) ----------------
# factor-decomposition with 2 factors and colwise normal-gamma prior on the loadings
sigma_factor_cng <- specify_prior_sigma(data = data, type = "factor",
factor_factors = 2L, factor_priorfacloadtype = "colwiseng",
factor_heteroskedastic = FALSE, factor_priorhomoskedastic = matrix(c(0.5,0.5),
ncol(data), 2))

# cholesky-decomposition with Horseshoe prior on U
sigma_cholesky_dl <- specify_prior_sigma(data = data, type = "cholesky",
cholesky_U_prior = "HS", cholesky_heteroscedastic = FALSE)


# Estimate model with your prior configuration of choice
mod <- bvar(data, prior_sigma = sigma_factor_cng_sv, quiet = TRUE)

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# examples with stochastic volatility (heteroscedasticity) -----------------
# factor-decomposition with 2 factors and colwise normal-gamma prior on the loadings
sigma_factor_cng_sv <- specify_prior_sigma(data = data, type = "factor",
factor_factors = 2L, factor_priorfacloadtype = "colwiseng", factor_heteroskedastic = TRUE)

# cholesky-decomposition with Dirichlet-Laplace prior on U
sigma_cholesky_dl_sv <- specify_prior_sigma(data = data, type = "cholesky",
cholesky_U_prior = "DL", cholesky_DL_a = 0.5, cholesky_heteroscedastic = TRUE)

# examples without stochastic volatility (homoscedasticity) ----------------
# factor-decomposition with 2 factors and colwise normal-gamma prior on the loadings
sigma_factor_cng <- specify_prior_sigma(data = data, type = "factor",
factor_factors = 2L, factor_priorfacloadtype = "colwiseng",
factor_heteroskedastic = FALSE, factor_priorhomoskedastic = matrix(c(0.5,0.5),
ncol(data), 2))

# cholesky-decomposition with Horseshoe prior on U
sigma_cholesky_dl <- specify_prior_sigma(data = data, type = "cholesky",
cholesky_U_prior = "HS", cholesky_heteroscedastic = FALSE)


# Estimate model with your prior configuration of choice
mod <- bvar(data, prior_sigma = sigma_factor_cng_sv, quiet = TRUE)

Set identifying restrictions for the structural VAR parameters.

Description

Set identifying restrictions for the structural VAR parameters.

Usage

specify_structural_restrictions(
  x,
  restrictions_facload = NULL,
  restrictions_B0_inv_t = NULL,
  restrictions_B0 = NULL,
  restrictions_structural_coeff = NULL,
  restrictions_long_run_ir = NULL
)
specify_structural_restrictions(
  x,
  restrictions_facload = NULL,
  restrictions_B0_inv_t = NULL,
  restrictions_B0 = NULL,
  restrictions_structural_coeff = NULL,
  restrictions_long_run_ir = NULL
)

Arguments

x

An object of type bayesianVARs_bvar.

restrictions_facload

an M times r matrix of restrictions on the factor loadings. This is equivalent to the instantaneous effects of the factor shocks. Can only be used, if the factor decomposition for sigma was specified.

restrictions_B0_inv_t

an M times M matrix of restrictions on the instantaneous effects of the structural shocks. Columns correspond to shocks, rows to variables. Can only be used, if the cholesky decomposition for sigma was specified.

restrictions_B0

an M times M matrix of restrictions on the structural matrix. Can only be used, if the cholesky decomposition for sigma was specified.

restrictions_structural_coeff

a matrix of restrictions on the structural VAR coefficients. Its size should match the dimensions of x$PHI. Can only be used, if the cholesky decomposition for sigma was specified.

restrictions_long_run_ir

a matrix of restrictions on the long run impulse responses. The long run impulse responses are the sum of the impulse responses summed over all time horizons. Restrictions on the long run impulse responses can be specified for both the factor and the cholesky decomposition of sigma. In the case of a factor decomposition its size is expected to be M times r. In the case of a cholesky decomposition the size must be M times M.

Details

All restrictions_* entries have following meaning

NA:: an unrestricted entry.
0:: The entry at this position is restricted to be zero (i.e. an exclusion restriction).
A positive number:: The sign of this entry should be positive.
A negative number:: The sign of this entry should be negative.

The structural VAR(p) model is of the following form:

$\boldsymbol{y}^\prime_t \boldsymbol{B}_0 = \boldsymbol{x}^\prime_t\boldsymbol{\Phi} \boldsymbol{B}_0 + \boldsymbol{\omega}^\prime_t$

Author(s)

Stefan Haan [email protected]

Examples

train_data <- 100 * usmacro_growth[,c("GDPC1", "GDPCTPI", "GS1", "M2REAL", "CPIAUCSL")]
prior_sigma <- specify_prior_sigma(train_data, type="cholesky", cholesky_heteroscedastic=FALSE)
mod <- bvar(train_data, lags=5L, prior_sigma=prior_sigma, quiet=TRUE)

structural_restrictions <- specify_structural_restrictions(
 mod,
 restrictions_B0=rbind(
   c(1 ,NA,0 ,NA,NA),
   c(0 ,1 ,0 ,NA,NA),
   c(0 ,NA,1 ,NA,NA),
   c(0 ,0 ,NA,1 ,NA),
   c(0 ,0 ,0 ,0 ,1 )
 )
)
irf_structural <- irf(
 mod, ahead=8,
 structural_restrictions=structural_restrictions
)
plot(irf_structural)

train_data <- 100 * usmacro_growth[,c("GDPC1", "GDPCTPI", "GS1", "M2REAL", "CPIAUCSL")]
prior_sigma <- specify_prior_sigma(train_data, type="cholesky", cholesky_heteroscedastic=FALSE)
mod <- bvar(train_data, lags=5L, prior_sigma=prior_sigma, quiet=TRUE)

structural_restrictions <- specify_structural_restrictions(
 mod,
 restrictions_B0=rbind(
   c(1 ,NA,0 ,NA,NA),
   c(0 ,1 ,0 ,NA,NA),
   c(0 ,NA,1 ,NA,NA),
   c(0 ,0 ,NA,1 ,NA),
   c(0 ,0 ,0 ,0 ,1 )
 )
)
irf_structural <- irf(
 mod, ahead=8,
 structural_restrictions=structural_restrictions
)
plot(irf_structural)

Stable posterior draws

Description

stable_bvar() detects and discards all posterior draws of an bayesianVARs_bvar object that do not fulfill the stability condition: A VAR(p) model is considered as stable only if the eigenvalues of the companion form matrix lie inside the unit circle.

Usage

stable_bvar(object, quiet = FALSE)
stable_bvar(object, quiet = FALSE)

Arguments

object

A bayesianVARs_bvar object obtained via bvar().

quiet

logical indicating whether informative output should be omitted.

Value

An object of type bayesianVARs_bvar.

Examples

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

# Discard "unstable" draws
stable_mod <- stable_bvar(mod)

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

# Discard "unstable" draws
stable_mod <- stable_bvar(mod)

Summary method for bayesianVARs_bvar objects

Description

Summary method for bayesianVARs_bvar objects.

Usage

## S3 method for class 'bayesianVARs_bvar'
summary(object, quantiles = c(0.025, 0.25, 0.5, 0.75, 0.975), digits = 3L, ...)
## S3 method for class 'bayesianVARs_bvar'
summary(object, quantiles = c(0.025, 0.25, 0.5, 0.75, 0.975), digits = 3L, ...)

Arguments

object

A bayesianVARs_bvar object obtained via bvar().

quantiles

numeric vector which quantiles to compute.

digits

Single integer indicating the number of decimal places to be used for rounding the summary statistics. Negative values are not allowed.

...

Currently ignored!

Value

An object of type summary.bayesianVARs_bvar.

Examples

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate model
mod <- bvar(data, quiet = TRUE)

# Summary
sum <- summary(mod)
# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Estimate model
mod <- bvar(data, quiet = TRUE)

# Summary
sum <- summary(mod)

Summary statistics for bayesianVARs posterior draws.

Description

Summary statistics for bayesianVARs posterior draws.

Usage

## S3 method for class 'bayesianVARs_draws'
summary(object, quantiles = c(0.25, 0.5, 0.75), ...)
## S3 method for class 'bayesianVARs_draws'
summary(object, quantiles = c(0.25, 0.5, 0.75), ...)

Arguments

object

An object of class bayesianVARs_draws usually obtained through extractors like coef.bayesianVARs_bvar() and vcov.bayesianVARs_bvar().

quantiles

A vector of quantiles to evaluate.

...

Currently ignored.

Value

A list object of class bayesianVARs_draws_summary holding

mean: Vector or matrix containing the posterior mean.
sd: Vector or matrix containing the posterior standard deviation .
quantiles: Array containing the posterior quantiles.

Examples


# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]
# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

# Extract posterior draws of VAR coefficients
bvar_coefs <- coef(mod)

# Compute summary statistics
summary_stats <- summary(bvar_coefs)

# Compute summary statistics of VAR coefficients without using coef()
summary_stats <- summary(mod$PHI)

# Test which list elements of 'mod' are of class 'bayesianVARs_draws'.
names(mod)[sapply(names(mod), function(x) inherits(mod[[x]], "bayesianVARs_draws"))]

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]
# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

# Extract posterior draws of VAR coefficients
bvar_coefs <- coef(mod)

# Compute summary statistics
summary_stats <- summary(bvar_coefs)

# Compute summary statistics of VAR coefficients without using coef()
summary_stats <- summary(mod$PHI)

# Test which list elements of 'mod' are of class 'bayesianVARs_draws'.
names(mod)[sapply(names(mod), function(x) inherits(mod[[x]], "bayesianVARs_draws"))]

Summary method for bayesianVARs_predict objects

Description

Summary method for bayesianVARs_predict objects.

Usage

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

Arguments

object

A bayesianVARs_predict object obtained via predict.bayesianVARs_bvar().

...

Currently ignored!

Value

A summary.bayesianVARs_predict object.

Examples

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Split data in train and test
train <- data[1:(nrow(data)-4),]
test <- data[-c(1:(nrow(data)-4)),]

# Estimate model using train data only
mod <- bvar(train, quiet = TRUE)

# Simulate from 1-step ahead posterior predictive
predictions <- predict(mod, ahead = 1L)
summary(predictions)
# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]

# Split data in train and test
train <- data[1:(nrow(data)-4),]
test <- data[-c(1:(nrow(data)-4)),]

# Estimate model using train data only
mod <- bvar(train, quiet = TRUE)

# Simulate from 1-step ahead posterior predictive
predictions <- predict(mod, ahead = 1L)
summary(predictions)

Data from the US-economy

Description

21 selected quarterly time-series from 1953:Q1 to 2021:Q2. From FRED-QD data base (McCracken and Ng, 2021). Release date 2021-07. Data is transformed to be interpreted as growth-rates (first log-differences with the exception of interest rates, which are already growth rates).

Usage

usmacro_growth
usmacro_growth

Format

A matrix with 247 rows and 21 columns.

Source

Raw (untransformed) data available at https://www.stlouisfed.org/research/economists/mccracken/fred-databases, https://www.stlouisfed.org/-/media/project/frbstl/stlouisfed/research/fred-md/historical-vintages-of-fred-qd-2018-05-to-2024-12.zip.

References

McCracken, M. W. and Ng, S. (2021). FRED-QD: A Quarterly Database for Macroeconomic Research, Review, Federal Reserve Bank of St. Louis, 103(1), 1–44, doi:10.20955/r.103.1-44.

Extract posterior draws of the (time-varying) variance-covariance matrix for a VAR model

Description

Returns the posterior draws of the possibly time-varying variance-covariance matrix of a VAR estimated via bvar(). Returns the full paths if sv_keep="all" when calling bvar(). Otherwise, the draws of the variance-covariance matrix for the last observation are returned, only.

Usage

## S3 method for class 'bayesianVARs_bvar'
vcov(object, t = seq_len(nrow(object$logvar)), ...)
## S3 method for class 'bayesianVARs_bvar'
vcov(object, t = seq_len(nrow(object$logvar)), ...)

Arguments

object

An object of class bayesianVARs_bvar obtained via bvar().

t

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

...

Currently ignored.

Value

An array of class bayesianVARs_draws of dimension $T \times M \times M \times draws$ , where $T$ is the number of observations, $M$ the number of time-series and $draws$ the number of stored posterior draws.

Examples

# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]
# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

# Extract posterior draws of the variance-covariance matrix
bvar_vcov <- vcov(mod)
# Access a subset of the usmacro_growth dataset
data <- usmacro_growth[,c("GDPC1", "CPIAUCSL", "FEDFUNDS")]
# Estimate a model
mod <- bvar(data, sv_keep = "all", quiet = TRUE)

# Extract posterior draws of the variance-covariance matrix
bvar_vcov <- vcov(mod)

Package 'bayesianVARs'

Help Index

Extract or Replace Parts of a bayesianVARs_coef object

Description

Usage

Arguments

Value

Examples

Extract or Replace Parts of a bayesianVARs_draws object

Description

Usage

Arguments

Value

Examples

Markov Chain Monte Carlo Sampling for Bayesian Vectorautoregressions

Description

Usage

Arguments

Details

Value

MCMC algorithm

References

See Also

Examples

Extract VAR coefficients

Description

Usage

Arguments

Value

See Also

Examples

Retrieve the structural parameter B0\boldsymbol{B}_0B0​ samples from an IRF object.

Description

Usage

Arguments

Author(s)

See Also

Examples

Simulate fitted/predicted historical values for an estimated VAR model

Description

Usage

Arguments

Value

Examples

Impulse response functions

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Draw from generalized inverse Gaussian

Description

Usage

Arguments

Value

Examples

Pairwise visualization of out-of-sample posterior predictive densities.

Description

Usage

Arguments

Value

Note

See Also

Examples

Plot method for bayesianVARs_bvar

Description

Usage

Arguments

Value

See Also

Examples

Visualization of in-sample fit of an estimated VAR.

Description

Usage

Arguments

Value

Retrieve the structural parameter $\boldsymbol{B}_0$ samples from an IRF object.