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Increasing the power of specification tests

机译:提高规格测试的力量

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This paper shows how to increase the power of Hausman's (1978) specification test as well as the difference test in a large class of models. The idea is to impose the restrictions of the null and the alternative hypotheses when estimating the covariance matrix. If the null hypothesis is true then the proposed test has the same distribution as the existing ones in large samples. If the hypothesis is false then the proposed test statistic is larger with probability approaching one as the sample size increases in several important applications, including testing for endogeneity in the linear model. (C) 2018 Elsevier B.V. All rights reserved.
机译:本文展示了如何提高Hausman(1978)规格测试的力量以及大类模型中的差异测试。 该想法是在估计协方差矩阵时施加无效和替代假设的限制。 如果零假设是真的,则所提出的测试具有与大型样本中现有的测试相同的分布。 如果假设是假的,则所提出的测试统计学的概率较大,概率接近,因为样本大小在几个重要的应用中增加,包括在线性模型中的内核性测试。 (c)2018 Elsevier B.v.保留所有权利。

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