Clements, Michael P. and Galvão, Ana Beatriz (2012) Real-time forecasting of inflation and output growth with autoregressive models in the presence of data revisions. Journal of Applied Econometrics, Volume 28 (Number 3). pp. 458-477. doi:10.1002/jae.2274 ISSN 0883-7252.

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Abstract

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.

Item Type:	Journal Article
Divisions:	Faculty of Social Sciences > Economics
Journal or Publication Title:	Journal of Applied Econometrics
Publisher:	Wiley-Blackwell Publishing, Inc
ISSN:	0883-7252
Official Date:	May 2012
Dates:	Date Event May 2012 Published
Volume:	Volume 28
Number:	Number 3
Page Range:	pp. 458-477
DOI:	10.1002/jae.2274
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access

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