Improved tests for forecast comparisons in the presence of instabilities

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 Ro...

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Detalhes bibliográficos
Autor principal: Martins, L. F. (author)
Outros Autores: Perron, P. (author)
Formato: article
Idioma:eng
Publicado em: 2017
Assuntos:
Texto completo:http://hdl.handle.net/10071/12874
País:Portugal
Oai:oai:repositorio.iscte-iul.pt:10071/12874
Descrição
Resumo: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.