BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R
Martin Feldkircher and
No 395, Globalization Institute Working Papers from Federal Reserve Bank of Dallas
This document introduces the R library BGVAR to estimate Bayesian global vector autoregressions (GVAR) with shrinkage priors and stochastic volatility. The Bayesian treatment of GVARs allows us to include large information sets by mitigating issues related to overfitting. This improves inference and often leads to better out-of-sample forecasts. Computational efficiency is achieved by using C++ to considerably speed up time-consuming functions. To maximize usability, the package includes numerous functions for carrying out structural inference and forecasting. These include generalized and structural impulse response functions, forecast error variance and historical decompositions as well as conditional forecasts.
Keywords: Global Vector Autoregressions; Bayesian inference; time series analysis; R (search for similar items in EconPapers)
JEL-codes: C30 C50 C87 F40 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:fip:feddgw:88639
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