Using modifications of Lindeberg's interpolation technique, I propose a new identification-robust test for the structural parameter in a heteroskedastic instrumental variables model. While my analysis allows the number of instruments to be much larger than the sample size, it does not require many instruments, making my test applicable in settings that have not been well studied. Instead, the proposed test statistic has a limiting chi-squared distribution so long as an auxiliary parameter can be consistently estimated. This is possible using machine learning methods even when the number of instruments is much larger than the sample size. To improve power, a simple combination with the sup-score statistic of Belloni et al. (2012) is proposed. I point out that first-stage F-statistics calculated on LASSO selected variables may be misleading indicators of identification strength and demonstrate favorable performance of my proposed methods in both empirical data and simulation study.

翻译：通过改进林德伯格插值技术，本文提出了一种针对异方差工具变量模型中结构参数的新型识别稳健检验方法。本分析允许工具变量数量远大于样本容量，但无需大量工具变量，使得该检验方法适用于尚未被充分研究的场景。只要辅助参数能够被一致估计，所提出的检验统计量就具有渐近卡方分布特性。即使工具变量数量远大于样本容量，利用机器学习方法仍可实现这一目标。为提升检验功效，本文提出将其与Belloni等人（2012）的sup-score统计量进行简单组合。需要指出的是，基于LASSO选择变量计算的第一阶段F统计量可能误导识别强度的判断。通过实证数据与模拟研究，验证了所提方法具有优越性能。