Evaluating Predictive Densities of US Output Growth and Inflation in a Large Macroeconomic Data Set

Barbara Rossi

No 689, Working Papers from Barcelona School of Economics

Abstract: We evaluate conditional predictive densities for U.S. output growth and inflation using a number of commonly used forecasting models that rely on a large number of macroeconomic predictors. More specifically, we evaluate how well conditional predictive densities based on the commonly used normality assumption fit actual realizations out-of-sample. Our focus on predictive densities acknowledges the possibility that, although some predictors can improve or deteriorate point forecasts, they might have the opposite effect on higher moments. We find that normality is rejected for most models in some dimension according to at least one of the tests we use. Interestingly, however, combinations of predictive densities appear to be correctly approximated by a normal density: the simple, equal average when predicting output growth and Bayesian model average when predicting inflation.

Keywords: structural change; predictive density evaluation; output growth forecasts; inflation forecasts (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 (search for similar items in EconPapers)
Date: 2015-09
New Economics Papers: this item is included in nep-for
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Related works:
Journal Article: Evaluating predictive densities of US output growth and inflation in a large macroeconomic data set (2014)
Working Paper: Evaluating predictive densities of U.S. output growth and inflation in a large macroeconomic data set (2013)
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