Affordable Access

Real-time forecasting of inflation and output growth with autoregressive models in the presence of data revisions

Wiley-Blackwell Publishing, Inc
Publication Date


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.

There are no comments yet on this publication. Be the first to share your thoughts.


Seen <100 times