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.

Package details

AuthorMaximilian 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>)
MaintainerMaximilian Boeck <maximilian.boeck@wu.ac.at>
LicenseGPL-3
Version2.2.4
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("BGVAR")

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BGVAR documentation built on July 20, 2021, 9:07 a.m.