MCMC Estimation of Bayesian Vectorautoregressions
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...
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
Markov Chain Monte Carlo Sampling for Bayesian Vectorautoregressions
Extract VAR coefficients
Retrieve the structural parameter samples from an I...
Plot method for bayesianVARs_bvar
Visualization of in-sample fit of an estimated VAR.
Impulse Responses Plot
Fan chart
Set identifying restrictions for the structural VAR parameters.
Stable posterior draws
Extract or Replace Parts of a bayesianVARs_coef object
Extract or Replace Parts of a bayesianVARs_draws object
Summary method for bayesianVARs_bvar objects
Summary statistics for bayesianVARs posterior draws.
Summary method for bayesianVARs_predict objects
Extract posterior draws of the (time-varying) variance-covariance matr...
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>.
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