Improved tests for forecast comparisons in the presence of instabilities
Martins, Luis Filipe
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Citation (published version)Luis Filipe Martins, Pierre Perron. 2016. "Improved Tests for Forecast Comparisons in the Presence of Instabilities." JOURNAL OF TIME SERIES ANALYSIS, v. 37, Issue 5, pp. 650-659 (10).
Of interest is comparing the out-of-sample forecasting performance of two competing models in the presence of possible instabilities. To that effect, we suggest using simple structural change tests, sup-Wald and UDmax for changes in the mean of the loss differences. It is shown that Giacomini and Rossi (2010) tests have undesirable power properties, power that can be low and non-increasing as the alternative becomes further from the null hypothesis. On the contrary, our statistics are shown to have higher monotonic power, especially the UDmax version. We use their empirical examples to show the practical relevance of the issues raised.
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