论文标题
线性模型中具有仪器变量的伪造自适应集，违反了排除或有条件的外生性限制
The Falsification Adaptive Set in Linear Models with Instrumental Variables that Violate the Exclusion or Conditional Exogeneity Restriction
论文作者
论文摘要
Masten and Poirier（2021）在线性模型中引入了伪造自适应集（FAS），该模型具有单个内源变量，该变量估计了多个相关的仪器变量（IVS）。 FA反映了基线模型的伪造引起的模型不确定性。我们表明，它适用于有条件的外生性假设成立并且无效仪器仅违反排除假设的情况。我们提出了一个广义的FA，当某些仪器违反排除假设和/或某些仪器违反条件外生性假设时，它反映了模型不确定性。在假设无效的仪器本身不是内源解释变量的假设下，如果至少有一种相关的仪器可以满足排除和条件外均假设，则保证这种广义的FAS可以包含感兴趣的参数。
Masten and Poirier (2021) introduced the falsification adaptive set (FAS) in linear models with a single endogenous variable estimated with multiple correlated instrumental variables (IVs). The FAS reflects the model uncertainty that arises from falsification of the baseline model. We show that it applies to cases where a conditional exogeneity assumption holds and invalid instruments violate the exclusion assumption only. We propose a generalized FAS that reflects the model uncertainty when some instruments violate the exclusion assumption and/or some instruments violate the conditional exogeneity assumption. Under the assumption that invalid instruments are not themselves endogenous explanatory variables, if there is at least one relevant instrument that satisfies both the exclusion and conditional exogeneity assumptions then this generalized FAS is guaranteed to contain the parameter of interest.
