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BGVAR: Bayesian Global Vector Autoregressions
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>.
Getting started
Browse package contents
Package details |
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| Author | Maximilian Boeck [aut, cre] (<https://orcid.org/0000-0001-6024-8305>), Martin Feldkircher [aut] (<https://orcid.org/0000-0002-5511-9215>), Florian Huber [aut] (<https://orcid.org/0000-0002-2896-7921>), Darjus Hosszejni [ctb] (<https://orcid.org/0000-0002-3803-691X>) |
| Maintainer | Maximilian Boeck <maximilian.boeck@da-vienna.ac.at> |
| License | GPL-3 |
| Version | 2.5.2 |
| URL | https://github.com/mboeck11/BGVAR |
| Package repository | View on CRAN |
| Installation |
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