论文标题
两个预测是否具有相同的条件期望准确性？
Can Two Forecasts Have the Same Conditional Expected Accuracy?
论文作者
论文摘要
测试Giacomini和White（2006）提出的成对预测模型的均等预测精度的方法假设，使用固定宽度的滚动窗口估算了基础预测模型的参数，并在零假设中包含参数估计的效果。我们表明，两个预测模型的有条件预期的损失差异是Martingale差异序列的必要条件是，结果是两个预测的简单平均值。当预测包含参数估计错误时，这意味着结果的条件均值必须是过去估计错误的函数 - 在许多情况下，这种情况失败了。我们还表明，即使没有参数估计的许多类型的随机过程，零也可能失败。
The approach for testing equal predictive accuracy for pairs of forecasting models proposed by Giacomini and White (2006) assumes that the parameters of the underlying forecasting models are estimated using a rolling window of fixed width and incorporates the effect of parameter estimation in the null hypothesis. We show that a necessary and sufficient condition for the conditionally expected loss differential of two forecasting models to be a martingale difference sequence is that the outcome is a simple average of the two forecasts. When the forecasts contain parameter estimation errors, this means that the conditional mean of the outcome has to be a function of past estimation errors--a condition that fails in many situations. We also show that the null can fail even in the absence of parameter estimation for many types of stochastic processes in common use.
