In “Vector Autoregression (VAR) Models”, we outlined the estimation of Vector Autoregression (VAR) models, which are frequently used in the study of macroeconomic data. Since VARs frequently require estimation of a large number of parameters, a common problem is that estimates, forecasts, and impulse responses are imprecise.

One approach to solving this problem is to introduce non-data information into the analysis. Bayesian VAR (BVAR) methods (Litterman 1986; Doan, Litterman, and Sims 1984; Sims and Zha 1998; Koop and Korobilis 2010; Giannone, Lenza and Primiceri 2014) are a popular approach for achieving this, since Bayesian priors provide a logical and consistent way to include non-data information.

The remainder of this discussion describes the Bayesian treatment of the VAR model. We first describe the set of EViews tools for estimating and working with BVARs and provide examples of the approach. This first section assumes that you are familiar with the various methods outlined in the literature. The remaining section outlines the methods in somewhat more detail.