Evaluating real-time forecasts in real-time

Abstract

The accuracy of real-time forecasts of macroeconomicvariables that are subject to revisions may crucially depend on thechoice of data used to compare the forecasts against. We put forwarda flexible time-varying parameter regression framework to obtainearly estimates of the final value of macroeconomic variables basedupon the initial data release that may be used as actuals in currentforecast evaluation. We allow for structural changes in theregression parameters to accommodate benchmark revisions anddefinitional changes, which fundamentally change the statisticalproperties of the variable of interest, including the relationshipbetween the final value and the initial release. The usefulness ofour approach is demonstrated through an empirical applicationcomparing the accuracy of forecasts of US GDP growth rates from theSurvey of Professional Forecasters and the Greenbook.forecast evaluation;Bayesian estimation;structural breaks;data revision;parameter uncertainty