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.
| Version: |
2.5.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
abind, bayesm, coda, GIGrvg, graphics, knitr, MASS, Matrix, methods, parallel, Rcpp (≥ 1.0.3), RcppParallel, readxl, stats, stochvol (≥ 3.0.3), utils, xts, zoo |
| LinkingTo: |
Rcpp, RcppArmadillo, RcppProgress, RcppParallel, stochvol, GIGrvg |
| Suggests: |
rmarkdown, testthat (≥ 2.1.0) |
| Published: |
2022-05-02 |
| Author: |
Maximilian Boeck 
[aut, cre],
Martin Feldkircher
[aut],
Florian Huber
[aut],
Darjus Hosszejni
[ctb] |
| Maintainer: |
Maximilian Boeck <maximilian.boeck at da-vienna.ac.at> |
| BugReports: |
https://github.com/mboeck11/BGVAR/issues |
| License: |
GPL-3 |
| URL: |
https://github.com/mboeck11/BGVAR |
| NeedsCompilation: |
yes |
| SystemRequirements: |
C++11, GNU make |
| Language: |
en-US |
| Citation: |
BGVAR citation info |
| Materials: |
README NEWS |
| In views: |
TimeSeries |
| CRAN checks: |
BGVAR results |
Documentation:
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