Forecasting in the Presence of Instabilities: How Do We Know Whether Models Predict Well and How to Improve Them
Barbara Rossi ()
No 14472, CEPR Discussion Papers from C.E.P.R. Discussion Papers
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: business cycles; Density forecasts; Forecast Confidence Intervals; Forecasting; great recession; inflation; Instabilities; output growth; Structural Breaks; Time variation (search for similar items in EconPapers)
JEL-codes: D1 E21 E4 E52 H31 I3 (search for similar items in EconPapers)
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Working Paper: Forecasting in the Presence of Instabilities: How Do We Know Whether Models Predict Well and How to Improve Them (2019)
Working Paper: Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them (2019)
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