论文标题
使用通用Bierens最大统计量通过条件力矩限制标识的参数的推断
Inference for parameters identified by conditional moment restrictions using a generalized Bierens maximum statistic
论文作者
论文摘要
许多经济小组和动态模型，例如理性行为和Euler方程，暗示着感兴趣的参数是通过有条件的力矩限制来确定的。我们介绍了一种新颖的推理方法，没有任何关于哪种调节仪器弱或无关的信息。在Bierens（1990）的基础上，我们提出了惩罚的最大统计数据，并将引导性推断与模型选择结合在一起。我们的方法通过求解数据依赖性的最大值问题来调整参数选择来优化渐近功率。基于经验例子，广泛的蒙特卡洛实验证明了我们的推理程序在多大程度上优于文献中可用的过程。
Many economic panel and dynamic models, such as rational behavior and Euler equations, imply that the parameters of interest are identified by conditional moment restrictions. We introduce a novel inference method without any prior information about which conditioning instruments are weak or irrelevant. Building on Bierens (1990), we propose penalized maximum statistics and combine bootstrap inference with model selection. Our method optimizes asymptotic power by solving a data-dependent max-min problem for tuning parameter selection. Extensive Monte Carlo experiments, based on an empirical example, demonstrate the extent to which our inference procedure is superior to those available in the literature.
