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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2.4.3 by Maximilian Boeck, 3 months ago

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Authors: Maximilian Boeck [aut, cre] , Martin Feldkircher [aut] , Florian Huber [aut] , Darjus Hosszejni [ctb]

Documentation:   PDF Manual  

Task views: Time Series Analysis

GPL-3 license

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

Suggests rmarkdown, testthat

Linking to Rcpp, RcppArmadillo, RcppProgress, RcppParallel, stochvol, GIGrvg

System requirements: C++11, GNU make

See at CRAN