论文标题
仪器变量回归中渐近测试的效率损失
Efficiency Loss of Asymptotically Efficient Tests in an Instrumental Variables Regression
论文作者
论文摘要
在仪器变量模型中，分数统计量可以针对参数空间部分的任何替代方案进行界定。这些区域涉及对第一阶段回归系数和降低形式协方差矩阵的限制。因此，尽管在标准的渐进剂下，Lagrange乘数测试可以具有接近尺寸的功率。此信息损失限制了仅使用安德森·罗宾和分数统计数据的条件测试的功能。条件的准类比率测试也遭受了严重的损失，因为它可以为任何替代方案限制。 发生急剧功率损失的必要条件是，降低形式的协方差基质的遗传学具有相反符号的特征值。这些案例表示不可能的设计（ID）。我们通过将我们的理论应用于替代之间跨期弹性的问题（IES）来表明这是在实践中发生的。在Yogo（2004}和Andrews（2016）研究的11个国家中，有9个与95 \％级别的ID保持一致。
In an instrumental variable model, the score statistic can be bounded for any alternative in parts of the parameter space. These regions involve a constraint on the first-stage regression coefficients and the reduced-form covariance matrix. Consequently, the Lagrange Multiplier test can have power close to size, despite being efficient under standard asymptotics. This information loss limits the power of conditional tests which use only the Anderson-Rubin and the score statistic. The conditional quasi-likelihood ratio test also suffers severe losses because it can be bounded for any alternative. A necessary condition for drastic power loss to occur is that the Hermitian of the reduced-form covariance matrix has eigenvalues of opposite signs. These cases are denoted impossibility designs (ID). We show this happens in practice, by applying our theory to the problem of inference on the intertemporal elasticity of substitution (IES). Of eleven countries studied by Yogo (2004} and Andrews (2016), nine are consistent with ID at the 95\% level.
