Forecasting and Tracking Real-Time Data Revisions in Inflation Persistence

The purpose of this paper is to examine the forecasting ability of sixty-two vintages of revised real-time PCE and core PCE using nonparametric methodologies. The combined fields of real-time data and nonparametric forecasting have not been previously explored with rigor, which this paper remedies. The contributions of this paper are on the three fronts of (i.) analysis of real-time data; (ii.) the additional benefits of using nonparametric econometrics to examine real-time data; and (iii.) nonparametric forecasting with real-time data. Regarding the analysis of real-time data revisions, this paper finds that the third quarter releases of real-time data have the largest number of data revisions. Secondly, nonparametric regressions are beneficial in utilizing the information provided by data revisions, which typically are just a few tenths in magnitude but are significant enough to statistically affect regression results. The deviations in window widths can be useful in identifying potential problematic time periods such as a large spike in oil prices. The third and final front of this paper regards nonparametric forecasting and the best performing real-time data release with the three local nonparametric forecasting methods outperforming the parametric benchmark forecasts. Lastly, this paper shows that the best performing quarterly-release of real-time data is dependent on the benchmark revision periods. For vintages 1996:Q1 to 2003:Q3, the second quarter real-time data releases produce the smaller RMSE 58% of the time and for vintages 2003:Q4 to 2011:Q2, the third quarter real-time data releases produce forecasts with smaller RMSE approximately 60% of the time.