Forecasting with real-time macroeconomic data: the ragged-edge problem and revisions

Abstract

Real-time macroeconomic data are typically incomplete for today and the immediate past (‘ragged edge’) and subject to revision. To enable more timely forecasts the recent missing data have to imputed. In the context of the U.S. leading index we assess four alternative models, paying explicit attention to publication lags and data revisions. We conclude that the univariate imputation method in levels adopted by The Conference Board can be improved upon.

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Cite this paper

@inproceedings{Bouwman2009ForecastingWR, title={Forecasting with real-time macroeconomic data: the ragged-edge problem and revisions}, author={Kees E. Bouwman}, year={2009} }