半参数模型的一致异方差稳健LM型规格检验

I. Korolev
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引用次数: 2

摘要

本文建立了半参数条件平均模型的一致异方差鲁棒拉格朗日乘数(LM)型规范检验。一致性是通过使用级数方法将一个条件矩约束转化为越来越多的无条件矩约束来实现的。所提出的检验统计量易于计算,并且是零下的渐近标准正态。与先前关于参数模型中基于序列的规格检验的文献相比,我依赖于序列估计量的投影性质并推导出检验统计量的不同归一化。与Gupta(2018)最近的测试相比,我使用了一种不同的方法来计算异方差。我使用蒙特卡罗研究证明,与现有测试相比,我的测试具有优越的有限样本性能。我将测试应用于Yatchew和No(2001)的半参数汽油需求规格之一,并没有发现反对它的证据。
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A Consistent Heteroskedasticity Robust LM Type Specification Test for Semiparametric Models
This paper develops a consistent heteroskedasticity robust Lagrange Multiplier (LM) type specification test for semiparametric conditional mean models. Consistency is achieved by turning a conditional moment restriction into a growing number of unconditional moment restrictions using series methods. The proposed test statistic is straightforward to compute and is asymptotically standard normal under the null. Compared with the earlier literature on series-based specification tests in parametric models, I rely on the projection property of series estimators and derive a different normalization of the test statistic. Compared with the recent test in Gupta (2018), I use a different way of accounting for heteroskedasticity. I demonstrate using Monte Carlo studies that my test has superior finite sample performance compared with the existing tests. I apply the test to one of the semiparametric gasoline demand specifications from Yatchew and No (2001) and find no evidence against it.
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