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 . 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.


Reference manual

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2.1.5 by Maximilian Boeck, 5 months ago

Browse source code at

Authors: Maximilian Boeck [aut, cre] , Martin Feldkircher [aut] , Florian Huber [aut] , Christopher Sims [ctb]

Documentation:   PDF Manual  

Task views: Time Series Analysis, Bayesian Inference

GPL-3 license

Imports abind, bayesm, coda, GIGrvg, graphics, knitr, MASS, Matrix, methods, parallel, Rcpp, stats, stochvol, utils, xts, zoo

Suggests rmarkdown, testthat

Linking to Rcpp, RcppArmadillo, RcppProgress, stochvol, GIGrvg

System requirements: C++11

See at CRAN