REAL-TIME FORECASTING OF INFLATION AND OUTPUT GROWTH WITH AUTOREGRESSIVE MODELS IN THE PRESENCE OF DATA REVISIONS
Article first published online: 14 MAY 2012
Copyright © 2012 John Wiley & Sons, Ltd.
Journal of Applied Econometrics
Volume 28, Issue 3, pages 458–477, April/May 2013
How to Cite
Clements, M. P. and Galvão, A. B. (2013), REAL-TIME FORECASTING OF INFLATION AND OUTPUT GROWTH WITH AUTOREGRESSIVE MODELS IN THE PRESENCE OF DATA REVISIONS. J. Appl. Econ., 28: 458–477. doi: 10.1002/jae.2274
- Issue published online: 26 MAR 2013
- Article first published online: 14 MAY 2012
- Manuscript Revised: 15 DEC 2011
- Manuscript Received: 9 DEC 2010
We examine how the accuracy of real-time forecasts from models that include autoregressive terms can be improved by estimating the models on ‘lightly revised’ data instead of using data from the latest-available vintage. The benefits of estimating autoregressive models on lightly revised data are related to the nature of the data revision process and the underlying process for the true values. Empirically, we find improvements in root mean square forecasting error of 2–4% when forecasting output growth and inflation with univariate models, and of 8% with multivariate models. We show that multiple-vintage models, which explicitly model data revisions, require large estimation samples to deliver competitive forecasts. Copyright © 2012 John Wiley & Sons, Ltd.