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 with high dimensional conditioning instruments. We develop a novel inference method for the parameters identified by conditional moment restrictions, where the dimension of the conditioning instruments is high and there is no 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 the asymptotic power against a set of $n^{-1/2}$-local alternatives of interest by solving a data-dependent max-min problem for tuning parameter selection. We demonstrate the efficacy of our method by two empirical examples: the elasticity of intertemporal substitution and rational unbiased reporting of ability status. Extensive Monte Carlo experiments based on the first empirical example show that our inference procedure is superior to those available in the literature in realistic settings.

翻译：许多经济面板和动态模型，例如理性行为与欧拉方程，隐含了参数由条件矩约束所识别，且该条件矩约束使用了高维度的条件工具变量。我们针对此类参数提出了一种全新的推断方法，该方法适用于条件工具变量维度较高且缺乏关于哪些工具变量为弱工具或无关变量的先验信息的情形。基于Bierens (1990)的研究，我们提出了惩罚极大统计量，并将自助法推断与模型选择相结合。通过求解一个依赖于数据的极大极小问题来选择调优参数，该方法针对一组感兴趣的$n^{-1/2}$局部备择假设，优化了渐近检验势。我们通过两个实证案例展示了该方法的有效性：跨期替代弹性和能力状态的理性无偏报告。基于第一个实证案例的大量蒙特卡洛实验表明，在现实情境下，我们的推断方法优于现有文献中的方法。