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

Kees E. Bouwman and Jan Jacobs

Journal of Macroeconomics, 2011, vol. 33, issue 4, 784-792

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 be imputed. The paper presents a state-space model that can deal with publication lags and data revisions. The framework is applied to the US leading index. We conclude that including even a simple model of data revisions improves the accuracy of the imputations and that the univariate imputation method in levels adopted by The Conference Board can be improved upon.

Keywords: Data revisions; Publication lags; Data imputations; Leading index; State space models; Kalman filter (search for similar items in EconPapers)
JEL-codes: C53 E32 E37 (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (9)

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Working Paper: Forecasting with real-time macroeconomic data: the ragged-edge problem and revisions (2005)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jmacro:v:33:y:2011:i:4:p:784-792

DOI: 10.1016/j.jmacro.2011.04.002

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