Journal of Economic Literature
ISSN 0022-0515 (Print) | ISSN 2328-8175 (Online)
Forecasting in the Presence of Instabilities: How We Know Whether Models Predict Well and How to Improve Them
Journal of Economic Literature
vol. 59,
no. 4, December 2021
(pp. 1135–90)
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 2007–08, 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.Citation
Rossi, Barbara. 2021. "Forecasting in the Presence of Instabilities: How We Know Whether Models Predict Well and How to Improve Them." Journal of Economic Literature, 59 (4): 1135–90. DOI: 10.1257/jel.20201479Additional Materials
JEL Classification
- C51 Model Construction and Estimation
- C53 Forecasting Models; Simulation Methods
- E31 Price Level; Inflation; Deflation
- E32 Business Fluctuations; Cycles
- E37 Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
- F37 International Finance Forecasting and Simulation: Models and Applications