Evaluating Real-Time VAR Forecasts with an Informative democratic Prior

This paper proposes Bayesian forecasting in a vector autoregression using a democratic prior. This prior is chosen to match the predictions of survey respondents. In particular, the unconditional mean for each series in the vector autoregression is centered around long-horizon survey forecasts. Heavy shrinkage toward the democratic prior is found to give good real-time predictions of a range of macroeconomic variables, as these survey projections are good at quickly capturing endpoint-shifts. Department of Economics, Johns Hopkins University, Baltimore MD 21218; (410) 516 5728; [email protected]. I am grateful to the editor, three anonymous referees, Todd Clark, Neil Ericsson, Jon Faust, Gary Koop, Lucrezia Reichlin and Frank Schorfheide for their helpful comments on earlier drafts. All errors are my sole responsibility.