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.

翻译：许多经济面板与动态模型（如理性行为与欧拉方程）意味着目标参数由条件矩约束所识别。本文提出了一种无需预先获知哪些条件工具变量存在弱相关性或无关性的新型推断方法。基于Bierens (1990) 的研究框架，我们构建了惩罚化最大统计量，并将自助法推断与模型选择相结合。该方法通过求解数据依赖的极大极小优化问题进行调参选择，从而实现了渐近功效的最优化。基于实证案例的大规模蒙特卡洛实验表明，我们的推断方法在多个维度上优于现有文献中的同类方法。