Michael P. Clements
Ana Beatriz Galvão

real-time forecasting of inflation and output growth with autoregressive models in the presence of data revisions (replication data)

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.

Data and Resources

Suggested Citation

Clements, Michael P.; Galvão, Ana Beatriz (2013): REAL-TIME FORECASTING OF INFLATION AND OUTPUT GROWTH WITH AUTOREGRESSIVE MODELS IN THE PRESENCE OF DATA REVISIONS (replication data). Version: 1. Journal of Applied Econometrics. Dataset. http://dx.doi.org/10.15456/jae.2022320.0731852787