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.

Additional Metadata
Keywords Bayesian estimation, data revision, forecast evaluation, parameter uncertainty, structural breaks
JEL Bayesian Analysis (jel C11), Time-Series Models; Dynamic Quantile Regressions (jel C22), Forecasting and Other Model Applications (jel C53), Methodology for Collecting, Estimating, and Organizing Macroeconomic Data (jel C82), Measurement and Data on National Income and Product Accounts (NIPA) and Wealth (jel E01), Forecasting and Simulation (jel E27)
Persistent URL
Series Econometric Institute Research Papers
Journal Report / Econometric Institute, Erasmus University Rotterdam
van Dijk, D.J.C, Franses, Ph.H.B.F, & Ravazzolo, F. (2007). Evaluating real-time forecasts in real-time (No. EI 2007-33). Report / Econometric Institute, Erasmus University Rotterdam. Retrieved from

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