Hierarchical Bayesian Vector Autoregression
Dummy prior settings
Forecast settings
Impulse response settings and identification
Metropolis-Hastings settings
Minnesota prior settings
Prior settings
BVAR: Hierarchical Bayesian vector autoregression
Hierarchical Bayesian vector autoregression
Methods for coda Markov chain Monte Carlo objects
Coefficient and VCOV methods for Bayesian VARs
Retrieve companion matrix from a Bayesian VAR
Density methods for Bayesian VARs
Fitted and residual methods for Bayesian VARs
FRED transformation and subset helper
Historical decomposition
Impulse response and forecast error methods for Bayesian VARs
Log-Likelihood method for Bayesian VARs
Parallel hierarchical Bayesian vector autoregression
Plotting method for Bayesian VARs
Plotting method for Bayesian VAR predictions
Plotting method for Bayesian VAR impulse responses
Predict method for Bayesian VARs
Model fit in- and out-of-sample
Summary method for Bayesian VARs
Widely applicable information criterion (WAIC) for Bayesian VARs
Estimation of hierarchical Bayesian vector autoregressive models following Kuschnig & Vashold (2021) <doi:10.18637/jss.v100.i14>. Implements hierarchical prior selection for conjugate priors in the fashion of Giannone, Lenza & Primiceri (2015) <doi:10.1162/REST_a_00483>. Functions to compute and identify impulse responses, calculate forecasts, forecast error variance decompositions and scenarios are available. Several methods to print, plot and summarise results facilitate analysis.