Abstract

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.

中文翻译：

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存在异方差和自相关的渐近 F 分布 Chow 检验

摘要

本研究在存在异方差和自相关的情况下提出了一种简单、可靠的 Chow 检验。该测试基于一系列异方差性和自相关稳健方差估计器，并具有精心设计的基函数。与经典正态线性回归中的 Chow 检验一样，所提出的检验采用标准 F 分布作为参考分布，这在固定平滑渐近线下是合理的。蒙特卡罗模拟表明，渐近 F 检验的零拒绝概率比卡方检验更接近标称水平。