Improved Tests for Forecast Comparisons in the Presence of Instabilities
针对模型预测比较中可能存在的结构变化，提出了基于损失差异均值变化的sup-Wald和UDmax检验，解决了现有检验在备择假设远离原假设时功效低且不单调的问题。
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 ( ) 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.