Working Paper: CEPR ID: DP10168
Authors: Atsushi Inoue; Lu Jin; Barbara Rossi
Abstract: While forecasting is a common practice in academia, government and business alike, practitioners are often left wondering how to choose the sample for estimating forecasting models. When we forecast inflation in 2014, for example, should we use the last 30 years of data or the last 10 years of data? There is strong evidence of structural changes in economic time series, and the forecasting performance is often quite sensitive to the choice of such window size. In this paper, we develop a novel method for selecting the estimation window size for forecasting. Specifically, we propose to choose the optimal window size that minimizes the forecaster's quadratic loss function, and we prove the asymptotic validity of our approach. Our Monte Carlo experiments show that our method performs quite well under various types of structural changes. When applied to forecasting US real output growth and inflation, the proposed method tends to improve upon conventional methods.
Keywords: forecasting; GDP growth; inflation; instabilities; structural change
JEL Codes: C22; C52; C53
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| choice of estimation window size (C51) | forecasting accuracy of output growth (O40) |
| optimal window size (C61) | forecasting performance (C53) |
| optimal window size (C61) | predictive ability of standard forecasting models (C53) |
| window size captures time variation in parameters (C22) | accurate forecasting (C53) |
| asset prices, housing starts, and building permits (R31) | forecasting output growth (O40) |
| unemployment measures (J64) | inflation forecasting (F37) |