MCMC Estimation of Bayesian Vectorautoregressions
Markov Chain Monte Carlo Sampling for Bayesian Vectorautoregressions
Extract VAR coefficients
Simulate fitted/predicted historical values for an estimated VAR model
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.
Fan chart
Posterior heatmaps for VAR coefficients or variance-covariance matrice...
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
Specify prior on PHI
Specify prior on Sigma
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 (2023) <doi:10.48550/arXiv.2206.04902>. 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>.
Useful links