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

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dc.contributor.author Rossi, Barbara, 1971-
dc.date.accessioned 2021-09-07T11:34:25Z
dc.date.available 2021-09-07T11:34:25Z
dc.date.issued 2021-07
dc.identifier.uri http://hdl.handle.net/10230/48402
dc.description.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.
dc.format.mimetype application/pdf
dc.language eng
dc.language.iso eng
dc.subject.other Forecasting
dc.subject.other Instabilities
dc.subject.other Time variation
dc.subject.other Inflation
dc.subject.other Structural breaks
dc.subject.other Density forecasts
dc.subject.other Great recession
dc.subject.other Forecast confidence intervals
dc.subject.other Output growth
dc.subject.other Business cycles
dc.title Forecasting in the presence of instabilities: how do we know whether models predict well and how to improve them
dc.type info:eu-repo/semantics/workingPaper
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/draft


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