What should researchers do when their baseline model is falsified? We recommend reporting the set of parameters that are consistent with minimally nonfalsified models. We call this the falsification adaptive set (FAS). This set generalizes the standard baseline estimand to account for possible falsification. Importantly, it does not require the researcher to select or calibrate sensitivity parameters. In the classical linear IV model with multiple instruments, we show that the FAS has a simple closed‐form expression that only depends on a few 2SLS coefficients. We apply our results to an empirical study of roads and trade. We show how the FAS complements traditional overidentification tests by summarizing the variation in estimates obtained from alternative nonfalsified models.

中文翻译：

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挽救伪造的工具变量模型

伪造基线模型后，研究人员应该怎么做？我们建议报告与最少非伪造模型一致的参数集。我们称其为伪造自适应集（FAS）。该集合概括了标准基线估计值，以应对可能的伪造。重要的是，它不需要研究人员选择或校准灵敏度参数。在具有多种仪器的经典线性IV模型中，我们证明了FAS具有简单的闭式表达式，仅取决于几个2SLS系数。我们将研究结果应用于道路和贸易的实证研究。我们通过总结从备选非伪造模型获得的估计值的变化，来展示FAS如何补充传统的过度识别测试。