Working Paper: CEPR ID: DP14472
Authors: Barbara Rossi
Abstract: This article provides guidance on how to evaluate and improve the forecasting ability of models in the presence of instabilities, which are widespread in economic time series. Empirically relevant examples include predicting the financial crisis of 2007-2008, as well as, more broadly, fluctuations in asset prices, exchange rates, output growth and inflation. In the context of unstable environments, I discuss how to assess models' forecasting ability; how to robustify models' estimation; and how to correctly report measures of forecast uncertainty. Importantly, and perhaps surprisingly, breaks in models' parameters are neither necessary nor sufficient to generate time variation in models' forecasting performance: thus, one should not test for breaks in models' parameters, but rather evaluate their forecasting ability in a robust way. In addition, local measures of models' forecasting performance are more appropriate than traditional, average measures.
Keywords: Forecasting; Instabilities; Time Variation; Inflation; Structural Breaks; Density Forecasts; Great Recession; Forecast Confidence Intervals; Output Growth; Business Cycles
JEL Codes: E4; E52; E21; H31; I3; D1
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| instabilities in forecasting performance (C53) | significant concern for economic models (E19) |
| traditional methods of evaluating forecasting models (C53) | poor predictive performance under instabilities (C62) |
| parameters of models do not need to exhibit structural breaks (C51) | influence forecasting performance (C53) |
| local measures of forecasting performance (C53) | more appropriate than average measures under unstable conditions (C52) |
| forecasting performance can change due to factors such as time-varying volatility (G17) | need for models to adapt (C59) |