Finite Sample Limited Information Inference Methods for Structural Equations and Models With Generated Regressors
提出针对工具变量估计的结构模型（如含未观测回归量的模型）的精确检验和置信集方法，即使在识别问题或弱工具变量下仍有效，并应用于托宾q模型和学业表现模型。
We propose exact tests and confidence sets for various structural models typically estimated by IV methods, such as models with unobserved regressors, which remain valid despite the presence of identification problems or weak instruments. Two approaches are considered: (1) an instrument substitution method, which generalizes the Anderson–Rubin procedure, and (2) a sample‐split method, that allows the use of “generated regressors.” Projection techniques are also proposed for inference on general parameter transformations. The asymptotic theory of the tests under weaker assumptions is discussed. Simulation results are presented. The suggested techniques are applied to a model of Tobin's q and to a model of academic performance.