Bayesian VARs: Specification Choices and Forecast Accuracy

Abstract

In this paper we discuss how the point and density forecasting performance of Bayesian VARs is affected by a number of specification choices. We adopt as a benchmark a common specification in the literature, a Bayesian VAR with variables entering in levels and a prior modeled along the lines of Sims and Zha (1998). We then consider optimal choice of the tightness, of the lag length and of both; evaluate the relative merits of modeling in levels or growth rates; compare alternative approaches to h-step ahead forecasting (direct, iterated and pseudo-iterated); discuss the treatment of the error variance and of cross-variable shrinkage; and assess rolling versus recursive estimation. Finally, we analyze the robustness of the results to the VAR size and composition (using also data for France, Canada and the UK, while the main analysis is for the US). We obtain a large set of empirical results, but the overall message is that we find very small losses (and sometimes even gains) from the adoption of specification choices that make BVAR modeling quick and easy, in particular for point forecasting. This finding could therefore further enhance the diffusion of the BVAR as an econometric tool for a vast range of applications.