Paper #1370

Título:

Evaluating predictive densities of U.S. output growth and inflation in a large macroeconomic data set

Autores:

Barbara Rossi y Tatevik Sekhposyan

Data:

Febrero 2013

Resumen:

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.

Palabras clave:

Predictive Density Evaluation, Structural Change, Output Growth Forecasts, Inflation Forecasts.

Códigos JEL:

C22, C52, C53

Área de investigación:

Macroeconomía y Economía Internacional

Publicado en:

International Journal of Forecasting, 30 (3), 662-682, July-September 2014

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