We develop an inference method for parameters identified by conditional moment restrictions, which are implied by economic models such as rational behavior and Euler equations. Building on Bierens (1990), we propose penalized maximum statistics and combine bootstrap inference with model selection. Our method is optimized to have nontrivial 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. Extensive Monte Carlo experiments show that our inference procedure becomes superior to those available in the literature when the number of irrelevant conditioning variables increases. We demonstrate the efficacy of our method by a proof of concept using two empirical examples: rational unbiased reporting of ability status and the elasticity of intertemporal substitution.

翻译：我们为有条件时间限制所确定的参数制定了一种推论方法,这种推论方法由理性行为和Euler等方程式等经济模型所隐含。在Bierens(1990年)的基础上,我们提出惩罚性最高统计数字,并将靴带推论与模型选择结合起来。我们的方法最优化,通过解决数据依赖的最大限度问题来调控参数选择,对一组美元-美元-美元-当地利益替代物拥有非三边性无症状能力。广泛的蒙特卡洛实验表明,当不相关的调节变量数量增加时,我们的推论程序比文献中可用的程序要优越。我们用两个经验实例,即合理公平地报告能力状况和时际替代的弹性,用概念证明我们的方法的有效性。