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摘要翻译：
本研究提出了一个简单、可靠的存在异方差和自相关的Chow检验。该检验是基于一个序列异方差和自相关的稳健方差估计器和明智地精心设计的基函数。与经典正态线性回归中的Chow检验一样，本文提出的检验采用标准F分布作为参考分布，在固定平滑渐近条件下是合理的。蒙特卡罗模拟表明，渐近F检验的零拒绝概率比卡方检验的零拒绝概率更接近名义水平。
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英文标题：
《An Asymptotically F-Distributed Chow Test in the Presence of
Heteroscedasticity and Autocorrelation》
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作者：
Yixiao Sun and Xuexin Wang
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最新提交年份：
2019
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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：
This study proposes a simple, trustworthy Chow test in the presence of heteroscedasticity and autocorrelation. The test is based on a series heteroscedasticity and autocorrelation robust variance estimator with judiciously crafted basis functions. Like the Chow test in a classical normal linear regression, the proposed test employs the standard F distribution as the reference distribution, which is justified under fixed-smoothing asymptotics. Monte Carlo simulations show that the null rejection probability of the asymptotic F test is closer to the nominal level than that of the chi-square test.
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PDF链接：
https://arxiv.org/pdf/1911.03771
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