Inference on local average treatment effects for misclassified treatment
当二元处理变量存在测量误差时，标准工具变量估计量不一致。本文通过利用外生变量识别测量误差分布，提出广义矩估计方法，并通过模拟和实证验证其有效性。
We develop point-identification for the local average treatment effect when the binary treatment contains a measurement error. The standard instrumental variable estimator is inconsistent for the parameter since the measurement error is nonclassical by construction. We correct the problem by identifying the distribution of the measurement error based on the use of an exogenous variable that can even be a binary covariate. The moment conditions derived from the identification lead to generalized method of moments estimation with asymptotically valid inferences. Monte Carlo simulations and an empirical illustration demonstrate the usefulness of the proposed procedure.