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

Authors: Barbara Rossi and Tatevik Sekhposyan

International Journal of Forecasting, Vol. 30, No 3, 662--682, January, 2014

We evaluate conditional predictive densities for US output growth and inflation using a number of commonly-used forecasting models that rely on large numbers 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 cause point forecasts to either improve or deteriorate, 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 approximated correctly by a normal density: the simple, equal average when predicting output growth, and the Bayesian model average when predicting inflation.

This paper originally appeared as Barcelona School of Economics Working Paper 689