Granger-Causal-Priority and Choice of Variables in Vector Autoregressions

Abstract

A researcher is interested in a set of variables that he wants to model with a vector autoregression and he has a dataset with more variables. Which variables from the dataset to include in the VAR, in addition to the variables of interest? This question arises in many applications of VARs, in prediction and impulse response analysis. We develop a Bayesian methodology to answer this question. We rely on the idea of Granger-causal-priority, related to the well-known concept of Granger-noncausality. The methodology is simple to use, because we provide closed-form expressions for the relevant posterior probabilities. Applying the methodology to the case when the variables of interest are output, the price level, and the short-term interest rate, we find remarkably similar results for the United States and the euro area.

Keywords: Bayesian model choice, Granger-causal-priority, Granger-noncausality, Structural vector autoregression, Vector autoregression

JEL Classification: C32, C52, E32