Package index • bsvars

bsvars: Bayesian Estimation of Structural Vector Autoregressive Models

Browse package information

bsvars-package bsvars: Bayesian Estimation of Structural Vector Autoregressive Models

Data

Upload sample data set

us_fiscal_lsuw: A 3-variable US fiscal system for the period 1948 Q1 – 2024 Q1

us_fiscal_ex: A 3-variable system of exogenous variables for the US fiscal model for the period 1948 Q1 – 2024 Q1

Model specification

Choose a model to work with

specify_bsvar: R6 Class representing the specification of the homoskedastic BSVAR model

specify_bsvar_mix: R6 Class representing the specification of the BSVAR model with a zero-mean mixture of normals model for structural shocks.

specify_bsvar_msh: R6 Class representing the specification of the BSVAR model with Markov Switching Heteroskedasticity.

specify_bsvar_sv: R6 Class representing the specification of the BSVAR model with Stochastic Volatility heteroskedasticity.

specify_bsvar_t: R6 Class representing the specification of the BSVAR model with t-distributed structural shocks.

More detailed model specification

Adjust or inspect the specified model

specify_data_matrices: R6 Class Representing DataMatricesBSVAR

specify_identification_bsvars: R6 Class Representing IdentificationBSVARs

specify_prior_bsvar: R6 Class Representing PriorBSVAR

specify_prior_bsvar_mix: R6 Class Representing PriorBSVARMIX

specify_prior_bsvar_msh: R6 Class Representing PriorBSVARMSH

specify_prior_bsvar_sv: R6 Class Representing PriorBSVARSV

specify_prior_bsvar_t: R6 Class Representing PriorBSVART

specify_starting_values_bsvar: R6 Class Representing StartingValuesBSVAR

specify_starting_values_bsvar_mix: R6 Class Representing StartingValuesBSVARMIX

specify_starting_values_bsvar_msh: R6 Class Representing StartingValuesBSVARMSH

specify_starting_values_bsvar_sv: R6 Class Representing StartingValuesBSVARSV

specify_starting_values_bsvar_t: R6 Class Representing StartingValuesBSVART

Estimation

Run Bayesian estimation of your model and inspect the outputs

estimate(<BSVAR>): Bayesian estimation of a homoskedastic Structural Vector Autoregression via Gibbs sampler

estimate(<BSVARMIX>): Bayesian estimation of a Structural Vector Autoregression with shocks following a finite mixture of normal components via Gibbs sampler

estimate(<BSVARMSH>): Bayesian estimation of a Structural Vector Autoregression with Markov-switching heteroskedasticity via Gibbs sampler

estimate(<BSVARSV>): Bayesian estimation of a Structural Vector Autoregression with Stochastic Volatility heteroskedasticity via Gibbs sampler

estimate(<BSVART>): Bayesian estimation of a homoskedastic Structural Vector Autoregression with t-distributed structural shocks via Gibbs sampler

estimate(<PosteriorBSVAR>): Bayesian estimation of a homoskedastic Structural Vector Autoregression via Gibbs sampler

estimate(<PosteriorBSVARMIX>): Bayesian estimation of a Structural Vector Autoregression with shocks following a finite mixture of normal components via Gibbs sampler

estimate(<PosteriorBSVARMSH>): Bayesian estimation of a Structural Vector Autoregression with Markov-switching heteroskedasticity via Gibbs sampler

estimate(<PosteriorBSVARSV>): Bayesian estimation of a Structural Vector Autoregression with Stochastic Volatility heteroskedasticity via Gibbs sampler

estimate(<PosteriorBSVART>): Bayesian estimation of a homoskedastic Structural Vector Autoregression with t-distributed structural shocks via Gibbs sampler

estimate(): Bayesian estimation of Structural Vector Autoregressions via Gibbs sampler

normalise_posterior(): Waggoner & Zha (2003) row signs normalisation of the posterior draws for matrix \(B\)

specify_posterior_bsvar: R6 Class Representing PosteriorBSVAR

specify_posterior_bsvar_mix: R6 Class Representing PosteriorBSVARMIX

specify_posterior_bsvar_msh: R6 Class Representing PosteriorBSVARMSH

specify_posterior_bsvar_sv: R6 Class Representing PosteriorBSVARSV

specify_posterior_bsvar_t: R6 Class Representing PosteriorBSVART

Posterior summaries

Analyse the posterior summaries of the posterior estimation outcomes

summary(<Forecasts>): Provides posterior summary of Forecasts

summary(<PosteriorBSVAR>): Provides posterior summary of homoskedastic Structural VAR estimation

summary(<PosteriorBSVARMIX>): Provides posterior summary of non-normal Structural VAR estimation

summary(<PosteriorBSVARMSH>): Provides posterior summary of heteroskedastic Structural VAR estimation

summary(<PosteriorBSVARSV>): Provides posterior summary of heteroskedastic Structural VAR estimation

summary(<PosteriorBSVART>): Provides posterior summary of Structural VAR with t-distributed shocks estimation

summary(<PosteriorFEVD>): Provides posterior summary of forecast error variance decompositions

summary(<PosteriorFitted>): Provides posterior summary of variables' fitted values

summary(<PosteriorHD>): Provides posterior summary of historical decompositions

summary(<PosteriorIR>): Provides posterior summary of impulse responses

summary(<PosteriorRegimePr>): Provides posterior summary of regime probabilities

summary(<PosteriorShocks>): Provides posterior summary of structural shocks

summary(<PosteriorSigma>): Provides posterior summary of structural shocks' conditional standard deviations

summary(<SDDRautoregression>): Provides summary of verifying hypotheses about autoregressive parameters

summary(<SDDRidMIX>): Provides summary of verifying shocks' normality

summary(<SDDRidMSH>): Provides summary of verifying homoskedasticity

summary(<SDDRidSV>): Provides summary of verifying homoskedasticity

summary(<SDDRidT>): Provides summary of verifying shocks' normality

summary(<SDDRvolatility>): Provides summary of verifying homoskedasticity

Forecasting

Predict future values of your variables

forecast(<PosteriorBSVAR>): Forecasting using Structural Vector Autoregression

forecast(<PosteriorBSVARMIX>): Forecasting using Structural Vector Autoregression

forecast(<PosteriorBSVARMSH>): Forecasting using Structural Vector Autoregression

forecast(<PosteriorBSVARSV>): Forecasting using Structural Vector Autoregression

forecast(<PosteriorBSVART>): Forecasting using Structural Vector Autoregression

forecast(): Forecasting using Structural Vector Autoregression

Structural analyses

Compute interpretable outcomes

compute_conditional_sd(<PosteriorBSVAR>): Computes posterior draws of structural shock conditional standard deviations

compute_conditional_sd(<PosteriorBSVARMIX>): Computes posterior draws of structural shock conditional standard deviations

compute_conditional_sd(<PosteriorBSVARMSH>): Computes posterior draws of structural shock conditional standard deviations

compute_conditional_sd(<PosteriorBSVARSV>): Computes posterior draws of structural shock conditional standard deviations

compute_conditional_sd(<PosteriorBSVART>): Computes posterior draws of structural shock conditional standard deviations

compute_conditional_sd(): Computes posterior draws of structural shock conditional standard deviations

compute_fitted_values(<PosteriorBSVAR>): Computes posterior draws from data predictive density

compute_fitted_values(<PosteriorBSVARMIX>): Computes posterior draws from data predictive density

compute_fitted_values(<PosteriorBSVARMSH>): Computes posterior draws from data predictive density

compute_fitted_values(<PosteriorBSVARSV>): Computes posterior draws from data predictive density

compute_fitted_values(<PosteriorBSVART>): Computes posterior draws from data predictive density

compute_fitted_values(): Computes posterior draws from data predictive density

compute_historical_decompositions(<PosteriorBSVAR>): Computes posterior draws of historical decompositions

compute_historical_decompositions(<PosteriorBSVARMIX>): Computes posterior draws of historical decompositions

compute_historical_decompositions(<PosteriorBSVARMSH>): Computes posterior draws of historical decompositions

compute_historical_decompositions(<PosteriorBSVARSV>): Computes posterior draws of historical decompositions

compute_historical_decompositions(<PosteriorBSVART>): Computes posterior draws of historical decompositions

compute_historical_decompositions(): Computes posterior draws of historical decompositions

compute_impulse_responses(<PosteriorBSVAR>): Computes posterior draws of impulse responses

compute_impulse_responses(<PosteriorBSVARMIX>): Computes posterior draws of impulse responses

compute_impulse_responses(<PosteriorBSVARMSH>): Computes posterior draws of impulse responses

compute_impulse_responses(<PosteriorBSVARSV>): Computes posterior draws of impulse responses

compute_impulse_responses(<PosteriorBSVART>): Computes posterior draws of impulse responses

compute_impulse_responses(): Computes posterior draws of impulse responses

compute_regime_probabilities(<PosteriorBSVARMIX>): Computes posterior draws of regime probabilities

compute_regime_probabilities(<PosteriorBSVARMSH>): Computes posterior draws of regime probabilities

compute_regime_probabilities(): Computes posterior draws of regime probabilities

compute_structural_shocks(<PosteriorBSVAR>): Computes posterior draws of structural shocks

compute_structural_shocks(<PosteriorBSVARMIX>): Computes posterior draws of structural shocks

compute_structural_shocks(<PosteriorBSVARMSH>): Computes posterior draws of structural shocks

compute_structural_shocks(<PosteriorBSVARSV>): Computes posterior draws of structural shocks

compute_structural_shocks(<PosteriorBSVART>): Computes posterior draws of structural shocks

compute_structural_shocks(): Computes posterior draws of structural shocks

compute_variance_decompositions(<PosteriorBSVAR>): Computes posterior draws of the forecast error variance decomposition

compute_variance_decompositions(<PosteriorBSVARMIX>): Computes posterior draws of the forecast error variance decomposition

compute_variance_decompositions(<PosteriorBSVARMSH>): Computes posterior draws of the forecast error variance decomposition

compute_variance_decompositions(<PosteriorBSVARSV>): Computes posterior draws of the forecast error variance decomposition

compute_variance_decompositions(<PosteriorBSVART>): Computes posterior draws of the forecast error variance decomposition

compute_variance_decompositions(): Computes posterior draws of the forecast error variance decomposition

Model diagnostics

Verify heteroskedasticity and autoregressive parameters (in preparation: structural identification)

verify_autoregression(<PosteriorBSVAR>): Verifies hypotheses involving autoregressive parameters

verify_autoregression(<PosteriorBSVARMIX>): Verifies hypotheses involving autoregressive parameters

verify_autoregression(<PosteriorBSVARMSH>): Verifies hypotheses involving autoregressive parameters

verify_autoregression(<PosteriorBSVARSV>): Verifies hypotheses involving autoregressive parameters

verify_autoregression(<PosteriorBSVART>): Verifies hypotheses involving autoregressive parameters

verify_autoregression(): Verifies hypotheses involving autoregressive parameters

verify_identification(<PosteriorBSVAR>): Verifies identification through heteroskedasticity or non-normality of of structural shocks

verify_identification(<PosteriorBSVARMIX>): Verifies identification through heteroskedasticity or non-normality of of structural shocks

verify_identification(<PosteriorBSVARMSH>): Verifies identification through heteroskedasticity or non-normality of of structural shocks

verify_identification(<PosteriorBSVARSV>): Verifies identification through heteroskedasticity or non-normality of of structural shocks

verify_identification(<PosteriorBSVART>): Verifies identification through heteroskedasticity or non-normality of of structural shocks

verify_identification(): Verifies identification through heteroskedasticity or non-normality of of structural shocks

verify_volatility(<PosteriorBSVAR>): Verifies heteroskedasticity of structural shocks equation by equation

verify_volatility(<PosteriorBSVARMIX>): Verifies heteroskedasticity of structural shocks equation by equation

verify_volatility(<PosteriorBSVARMSH>): Verifies heteroskedasticity of structural shocks equation by equation

verify_volatility(<PosteriorBSVARSV>): Verifies heteroskedasticity of structural shocks equation by equation

verify_volatility(): Verifies heteroskedasticity of structural shocks equation by equation

Plot your results

Prepare beautiful and informative plots for your analyses

plot(<Forecasts>): Plots fitted values of dependent variables

plot(<PosteriorFEVD>): Plots forecast error variance decompositions

plot(<PosteriorFitted>): Plots fitted values of dependent variables

plot(<PosteriorHD>): Plots historical decompositions

plot(<PosteriorIR>): Plots impulse responses

plot(<PosteriorRegimePr>): Plots estimated regime probabilities

plot(<PosteriorShocks>): Plots structural shocks

plot(<PosteriorSigma>): Plots structural shocks' conditional standard deviations

plot_ribbon(): Plots the median and an interval between two specified percentiles for a sequence of K random variables