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Performance of conditional Wald tests in IV regression with weak instruments

机译:有条件的Wald检验在使用弱仪器进行IV回归中的表现

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We compare the powers of five tests of the coefficient on a single endogenous regressor in instrumental variables regression. Following Moreira [2003, A conditional likelihood ratio test for structural models. Econometrica 71, 1027-1048], all tests are implemented using critical values that depend on a statistic which is sufficient under the null hypothesis for the (unknown) concentration parameter, so these conditional tests are asymptotically valid under weak instrument asymptotics. Four of the tests are based on k-class Wald statistics (two-stage least squares, LIML, Fuller's [Some properties of a modification of the limited information estimator. Econometrica 45, 939-953], and bias-adjusted TSLS); the fifth is Moreira's (2003) conditional likelihood ratio (CLR) test. The heretofore unstudied conditional Wald (CW) tests are found to perform poorly, compared to the CLR test: in many cases, the CW tests have almost no power against a wide range of alternatives. Our analysis is facilitated by a newalgorithm, presented here, for the computation of the asymptotic conditional p-value of the CLR test.
机译:我们在工具变量回归中比较单个内生回归变量上五个系数的检验的功效。继Moreira [2003年,结构模型的条件似然比检验。 [Econometrica 71,1027-1048],所有测试均使用临界值实施,该临界值取决于在(未知)浓度参数的零假设下足够的统计量,因此这些条件测试在弱仪器渐近性下渐近有效。其中四个测试基于k级Wald统计量(两阶段最小二乘法,LIML,Fuller [有限信息估计量的修改的某些属性。Econometrica45,939-953],以及偏差调整后的TSLS);第五是莫雷拉(2003)的条件似然比(CLR)检验。与CLR测试相比,迄今尚未研究的条件Wald(CW)测试的性能较差:在许多情况下,CW测试几乎无法针对多种替代方法进行测试。这里介绍的新算法有助于我们的分析,用于计算CLR检验的渐进条件p值。

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