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 Q2

us_fiscal_ex: A 3-variable system of exogenous variables for the US fiscal model for the period 1948 Q1 – 2024 Q2

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

us_fiscal_cond_forecasts: A matrix to be used in a conditional forecasting example including the projected values of total tax revenue that are projected to increase at an average quarterly sample growth rate. The other two columns are filled with NA values, which implies that the future values of the corresponding endogenous variables, namely government spending and GDP, will be forecasted given the provided projected values of total tax revenue. The matrix includes future values for the forecast horizon of two years for the US fiscal model for the period 2024 Q3 – 2026 Q2.

us_fiscal_ex_forecasts: A 3-variable system of exogenous variables' future values for the forecast horizon of two years for the US fiscal model for the period 2024 Q3 – 2026 Q2

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