Evaluating Real-Time Forecasts in Real-Time

Abstract

The accuracy of real-time forecasts of macroeconomic variables that are subject to revisions may crucially depend on the choice of data used to compare the forecasts against. We put forward a flexible time - varying parameter regression framework to obtain early estimates of the final value of macroeconomic variables based upon the initial data release that may be used as actuals in current forecast evaluation. We allow for structural changes in the regression parameters to accommodate benchmark revisions and definitional changes, which fundamentally change the statistical properties of the variable of interest, including the relationship between the final value and the initial release. The usefulness of our approach is demonstrated through an empirical application comparing the accuracy of forecasts of US GDP growth rates from the Survey of Professional Forecasters and the Greenbook.

Keywords: Data revision, forecast evaluation, parameter uncertainty, Bayesian estimation, structural breaks

JEL Classification: C11, C22, C53, C82, E01, E27