Paper #1711

Títol:

Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them

Autor:

Barbara Rossi

Data:

Novembre 2019 (Revisió: Juliol 2021)

Resum:

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.

Paraules clau:

forecasting, instabilities, time variation, inflation, structural breaks, density forecasts, great recession, forecast confidence intervals, output growth, business cycles

Codis JEL:

E4, E52, E21, H31, I3, D1

Àrea de Recerca:

Estadística, Econometria i Mètodes Quantitatius

Descarregar el paper en format PDF (698 Kb)