Testing parameters in GMM without assuming that they are identified
Testing parameters in GMM without assuming that they are identified
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DOI：
10.1111/j.1468-0262.2005.00610.x
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发表时间：
2005-07-01
期刊：
影响因子：
6.1
通讯作者：
Kleibergen, F
中科院分区：
文献类型：
--
作者：
Kleibergen, F
我们提出了一种广义矩量法（GMM）的拉格朗日乘子统计量，即，K统计量，它使用基于连续更新估计量的雅可比估计量，该估计量与矩的样本平均值渐近不相关。因此，它的渐近chi（2）分布在更广泛的情况下成立，如弱工具，而不是期望雅可比矩阵的标准满秩情况，在这种情况下，传统统计的渐近chi（2）分布是有效的。K统计量的行为在目标函数的拐点和最大值附近可能是虚假的。通过将K统计量与检验矩方程有效性的统计量相结合，并通过将Moreira（2003）的条件似然比统计量扩展到GMM，可以克服这一不足。本文对一个随机贴现因子模型中的风险厌恶参数进行了功效比较检验，并构造了它对消费增长和资产收益率序列的置信集。
We propose a generalized method of moments (GMM) Lagrange multiplier statistic, i.e., the K statistic, that uses a Jacobian estimator based on the continuous updating estimator that is asymptotically uncorrelated with the sample average of the moments. Its asymptotic chi(2) distribution therefore holds under a wider set of circumstances, like weak instruments, than the standard full rank case for the expected Jacobian under which the asymptotic chi(2) distributions of the traditional statistics are valid. The behavior of the K statistic can be spurious around inflection points and maxima of the objective function. This inadequacy is overcome by combining the K statistic with a statistic that tests the validity of the moment equations and by an extension of Moreira's (2003) conditional likelihood ratio statistic toward GMM. We conduct a power comparison to test for the risk aversion parameter in a stochastic discount factor model and construct its confidence set for observed consumption growth and asset return series.