Tips and tricks for Bayesian VAR models in gretl

Abstract

AbstractBayesian Vector Autoregressive models have become the natural response to the dense parametrization often required by multivariate time series modeling. However, the Bayesian approach is somehow new to the ecosystem: classical analysis of Vector Autoregressions (VARs) is natively supported and supplemented via addons and function packages, but a Bayesian counterpart in the form of equally general and advanced functions or, at a lower level, in the form of didactic example scripts is missing. This paper pursues the second route describing, via a replication exercise, how to perform basic Bayesian inference in using VARs, with particular reference to structural analysis. The contribution goes in the direction of providing new hints and tools, available to the user, for a more complete and up-to-date understanding of modern macroeconometrics.