Bayesian VARs: Specification Choices And Forecast Accuracy

In this paper, the authors examine how the forecasting performance of Bayesian VARs is affected by a number of specification choices. In the baseline case, they use a Normal-Inverted Wishart prior that, when combined with a (pseudo-) iterated approach, makes the analytical computation of multi-step forecasts feasible and simple, in particular when using standard and fixed values for the tightness and the lag length. This finding could therefore further enhance the diffusion of the BVAR as an econometric tool for a vast range of applications.

Provided by: Federal Reserve Bank of Cleveland Topic: Big Data Date Added: May 2011 Format: PDF

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