A NOVEL APPROACH TO PREDICTIVE ACCURACY TESTING IN NESTED ENVIRONMENTS
提出一种比较两个嵌套模型预测精度的新方法，解决了传统统计量渐近方差退化的问题，适用于异方差和混合预测变量场景，模拟显示在常见样本量下具有良好的检验功效和尺寸控制。
We introduce a new approach for comparing the predictive accuracy of two nested models that bypasses the difficulties caused by the degeneracy of the asymptotic variance of forecast error loss differentials used in the construction of commonly used predictive comparison statistics. Our approach continues to rely on the out of sample mean squared error loss differentials between the two competing models, leads to nuisance parameter-free Gaussian asymptotics, and is shown to remain valid under flexible assumptions that can accommodate heteroskedasticity and the presence of mixed predictors (e.g., stationary and local to unit root). A local power analysis also establishes their ability to detect departures from the null in both stationary and persistent settings. Simulations calibrated to common economic and financial applications indicate that our methods have strong power with good size control across commonly encountered sample sizes.