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摘要翻译：
本文提出了一种新的方法来比较两个嵌套模型的预测精度，该方法克服了在构造常用的预测比较统计量时由于预测误差损失微分的渐近方差的退化所带来的困难。我们的方法继续依赖于两个竞争模型之间的样本外MSE损失微分，导致了讨厌的无参数高斯渐近，并被证明在灵活的假设下仍然有效，这些假设可以适应异方差和混合预测器的存在（例如，平稳的和局部的单位根）。本地功率分析还建立了它在静止和持久设置中检测偏离空值的能力。对常见的经济和金融应用的仿真表明，我们的方法具有很强的能力，在常见的样本规模上具有良好的规模控制。
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英文标题：
《A Novel Approach to Predictive Accuracy Testing in Nested Environments》
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作者：
Jean-Yves Pitarakis
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最新提交年份：
2020
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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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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PDF链接：
https://arxiv.org/pdf/2008.08387
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