嵌套环境下预测精度检验的新方法
A Novel Approach to Predictive Accuracy Testing in Nested Environments
AI总结：
提出一种嵌套模型预测精度比较的新方法，通过样本外MSE损失差分规避渐近方差退化问题，在灵活假设下保持有效，模拟显示强功效与良好尺寸控制。
摘要：
我们提出了一种比较两个嵌套模型预测精度的新方法，该方法绕过了常用预测比较统计量构建中因预测误差损失差分的渐近方差退化所带来的困难。我们的方法继续依赖于两个竞争模型之间的样本外均方误差损失差分，得到无干扰参数的高斯渐近性，并证明其在可容纳异方差性和混合预测因子（如平稳和局部单位根）存在的灵活假设下仍然有效。局部功效分析还确立了其在平稳和持续设定下检测偏离原假设的能力。以常见经济和金融应用为基准的模拟表明，我们的方法在常见样本量下具有强大的功效和良好的尺寸控制。
英文摘要：
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 MSE 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 its 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.
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