Size-corrected Bootstrap Test after Pretesting for Exogeneity with Heteroskedastic or Clustered Data
Size-corrected Bootstrap Test after Pretesting for Exogeneity with Heteroskedastic or Clustered Data
复制标题
使用异方差或聚类数据预测试外生性后的大小校正引导测试
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Doko Tchatoka
中科院分区：
文献类型：
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作者：
Doko Tchatoka
在许多涉及工具变量的经验应用中，外生性预测检验已成为一种惯例，以确定普通最小二乘方法或两阶段最小二乘方法(2SLS)是否合适。Guggenberger(2010)表明，第二阶段t检验-基于第一阶段Durbin-Wu Hausman型外生性预测检验的结果-具有极端大小失真，当使用标准渐近临界值时，渐近大小等于1。在这篇文章中，我们首先证明了无论是有条件的还是无条件的，标准的野生Bootstrap程序对于两阶段检验和密切相关的收缩方法都是无效的，因此不是解决这种尺寸失真问题的可行方案。然后，我们提出了一种新的尺寸校正的野生自举方法，它结合了特定的野生自举临界值和适当的尺寸校正方法。在条件异方差或聚类性的意义下，我们建立了这个过程的一致有效性，所得到的检验达到了正确的渐近大小。蒙特卡罗模拟证实了我们的理论发现。特别是，我们提出的方法在许多情况下都比标准的基于2SLS的t-检验有显著的功率增益，特别是在识别能力不强的情况下。
Pretesting for exogeneity has become a routine in many empirical applications involving instrumental variables to decide whether the ordinary least squares or the two-stage least squares (2SLS) method is appropriate. Guggenberger (2010) shows that the second-stage t-test – based on the outcome of a Durbin-WuHausman type pretest for exogeneity in the first stage – has extreme size distortion with asymptotic size equal to 1 when the standard asymptotic critical values are used. In this paper, we first show that both conditional and unconditional on the data, the standard wild bootstrap procedures are invalid for the two-stage testing and a closely related shrinkage method, and therefore are not viable solutions to such size-distortion problem. Then, we propose a novel size-corrected wild bootstrap approach, which combines certain wild bootstrap critical values along with an appropriate size-correction method. We establish uniform validity of this procedure under either conditional heteroskedasticity or clustering in the sense that the resulting tests achieve correct asymptotic size. Monte Carlo simulations confirm our theoretical findings. In particular, our proposed method has remarkable power gains over the standard 2SLS-based t-test in many settings, especially when the identification is not strong.
类 Stein 2SLS 估计器
影响因子：
1.2
作者：
Hansen, Bruce E.
通讯作者：
Hansen, Bruce E.
DOI：
10.1146/annurev-economics-080218-025643
发表时间：
2019-01-01
期刊：
ANNUAL REVIEW OF ECONOMICS, VOL 11, 2019
影响因子：
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作者：
Andrews, Isaiah;Stock, James H.;Sun, Liyang
通讯作者：
Sun, Liyang