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: |
2.5.1 |
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: |
2022-09-02 |
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:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=BGVAR
to link to this page.