We distinguish between three different strategies for estimating forecasting equations with real-time data and argue that the most popular approach should generally be avoided. The point is illustrated with a model that uses current-quarter monthly industrial production, employment, and retail sales data to predict real GDP growth. When the model is estimated using either of our two alternative methods, its out-of-sample forecasting performance is superior to that obtained using conventional estimation and compares favorably with that of the Blue Chip consensus.
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© 2003 President and Fellows of Harvard College and the Massachusetts Institute of Technology