Bayesian Global Vector Autoregressions
Adding shocks to 'shockinfo' argument
Average Pairwise Cross-Sectional Correlations
BGVAR: Bayesian Global Vector Autoregressions
Estimation of Bayesian GVAR
Extract Model Coefficients of Bayesian GVAR
MCMC Convergence Diagnostics
Deviance Information Criterion
Read Data from Excel
Forecast Error Variance Decomposition
Extract Fitted Values of Bayesian GVAR
Create shockinfo argument
Generalized Forecast Error Variance Decomposition
Historical Decomposition
Impulse Response Function
Convert Input List to Matrix
Extract Log-likelihood of Bayesian GVAR
Compute Log-Predictive Scores
Convert Input Matrix to List
Graphical Summary of Output Created with bgvar
Predictions
Residual Autocorrelation Test
Extract Residuals of Bayesian GVAR
Compute Root Mean Squared Errors
Summary of Bayesian GVAR
Extract Variance-covariance Matrix of Bayesian GVAR
Estimation of Bayesian Global Vector Autoregressions (BGVAR) with different prior setups and the possibility to introduce stochastic volatility. Built-in priors include the Minnesota, the stochastic search variable selection and Normal-Gamma (NG) prior. For a reference see also Crespo Cuaresma, J., Feldkircher, M. and F. Huber (2016) "Forecasting with Global Vector Autoregressive Models: a Bayesian Approach", Journal of Applied Econometrics, Vol. 31(7), pp. 1371-1391 <doi:10.1002/jae.2504>. Post-processing functions allow for doing predictions, structurally identify the model with short-run or sign-restrictions and compute impulse response functions, historical decompositions and forecast error variance decompositions. Plotting functions are also available. The package has a companion paper: Boeck, M., Feldkircher, M. and F. Huber (2022) "BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R", Journal of Statistical Software, Vol. 104(9), pp. 1-28 <doi:10.18637/jss.v104.i09>.