Generic Conditions for Forecast Dominance
研究了在一致评分函数类下，一个预测方法主导另一个预测方法的理论条件，推导了均值泛函预测主导的新特征，允许信息集非嵌套和未校准预测，并通过金融和经济数据示例说明其经验相关性。
<b>Recent studies have analyzed whether one forecast method dominates another under a class of consistent scoring functions. While the existing literature focuses on empirical tests of forecast dominance, little is known about the theoretical conditions under which one forecast dominates another. To address this question, we derive a new characterization of dominance among forecasts of the mean functional. We present various scenarios under which dominance occurs. Unlike existing results, our results allow for the case that the forecasts’ underlying information sets are not nested, and allow for uncalibrated forecasts that suffer, for example, from model misspecification or parameter estimation error. We illustrate the empirical relevance of our results via data examples from finance and economics.</b>