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

Barbara Rossi

Economics Working Papers from Department of Economics and Business, Universitat Pompeu Fabra

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 (search for similar items in EconPapers)
JEL-codes: D1 E21 E4 E52 H31 I3 (search for similar items in EconPapers)
Date: 2019-11, Revised 2021-07
New Economics Papers: this item is included in nep-ets, nep-gen and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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Related works:
Working Paper: Forecasting in the Presence of Instabilities: How Do We Know Whether Models Predict Well and How to Improve Them (2020) Downloads
Working Paper: Forecasting in the Presence of Instabilities: How Do We Know Whether Models Predict Well and How to Improve Them (2019) Downloads
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