论文标题
嵌套环境中预测精度测试的一种新型方法
A Novel Approach to Predictive Accuracy Testing in Nested Environments
论文作者
论文摘要
我们介绍了一种新方法，以比较两个嵌套模型的预测准确性，该模型绕过了由于在构建常用的预测比较统计量的构建中使用的预测误差差差差差差差差差异的渐近方差而引起的困难。我们的方法继续依赖于两个竞争模型之间的样本MSE损失差异，从而导致滋扰参数无渐近的渐近造型，并显示在可以适应异性恋性的灵活假设下仍然有效，并且存在混合预测因子（例如，固定式和本地固定在单位根中）。本地权力分析还确定了其在固定和持续设置中都无法检测出零零的能力。对常见经济和财务应用校准的模拟表明，我们的方法具有强大的功率，并且在常见的样本量之间具有良好的尺寸控制。
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 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.
