summary

summary.bgvar

Output gives model information as well as some descriptive statistics on convergence properties, likelihood, serial autocorrelation in the errors and the average pairwise autocorrelation of cross-country residuals.

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>.

Maximilian Boeck

BGVAR

Bayesian Global Vector Autoregressions

Martin Feldkircher

Florian Huber

Darjus Hosszejni

summary function

<dl><dt>object</dt>
<dd>An object of class <code>bgvar</code>.</dd>
<dt>...</dt>
<dd>Additional arguments.</dd></dl>

Arguments

Author

Summary of Bayesian GVAR — summary

<dl>

<dt>object</dt>
<dd>An object of class <code>bgvar</code>.</dd>


<dt>...</dt>
<dd>Additional arguments.</dd>

</dl>

summary: Summary of Bayesian GVAR

Description

Usage

Value

Arguments

Author

See Also