Inference for Parameters Identified by Conditional Moment Restrictions Using a Generalized Bierens Maximum Statistic
提出一种新的推断方法，无需事先知道哪些工具变量弱或无关，通过惩罚最大统计量和自助法结合模型选择，优化渐近功效，蒙特卡洛实验显示优于现有方法。
Abstract 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. [C12, C36].