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.

Data imputations, Data revisions, Kalman filter, Leading index, Publication lags, State space models,
Journal of Macroeconomics
Erasmus Research Institute of Management

Bouwman, K.E, & Jacobs, J.P.A.M. (2011). Forecasting with real-time macroeconomic data: The ragged-edge problem and revisions. Journal of Macroeconomics, 33(4), 784–792. doi:10.1016/j.jmacro.2011.04.002