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>.
Version: 2.5.8 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: 2024-09-30 DOI: 10.32614/CRAN.package.BGVAR Author: Maximilian Boeck [aut, cre], Martin Feldkircher [aut], Florian Huber [aut], Darjus Hosszejni [ctb] Maintainer: Maximilian Boeck <maximilian.boeck at fau.de> BugReports: https://github.com/mboeck11/BGVAR/issues License: GPL-3 URL: https://github.com/mboeck11/BGVAR NeedsCompilation: yes SystemRequirements: 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.
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