Feasible IV regression without excluded instruments
提出一种线性积分条件矩估计量，能在没有排除工具变量的情况下实现一致估计，并通过蒙特卡洛模拟和实证例子展示了其良好的有限样本表现。
Summary The relevance condition of integrated conditional moment (ICM) estimators is significantly weaker than the conventional instrumental variable's in at least two respects: (1) consistent estimation without excluded instruments is possible, provided endogenous covariates are nonlinearly mean-dependent on exogenous covariates, and (2) endogenous covariates may be uncorrelated with but mean-dependent on instruments. These remarkable properties notwithstanding, multiplicative-kernel ICM estimators suffer diminished identification strength, large bias, and severe size distortions even for a moderately sized instrument vector. This paper proposes a computationally fast linear ICM estimator that better preserves identification strength in the presence of multiple instruments and a test of the ICM relevance condition. Monte Carlo simulations demonstrate a considerably better size control in the presence of multiple instruments and a favourably competitive performance in general. An empirical example illustrates the practical usefulness of the estimator, where estimates remain plausible when no excluded instrument is used.