000			01471nam a22001457a 4500
999			_c519725 _d519725
008			220429b ||||| |||| 00| 0 eng d
100			_aRossi, Barbara _932941
245			_aForecasting in the presence of instabilities: How we know whether models predict well and how to improve them
260			_aJournal of Economic Literature
300			_a59(4), Dec, 2021: p.1135-1190
520			_aThis 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. – Reproduced
773			_aJournal of Economic Literature
906			_aECONOMIC DEVELOPMENT
942			_cAR