Estimating overidentified linear models with heteroskedasticity and outliers
异方差与异常值下过度识别线性模型的估计
A large degree of overidentification causes severe bias in TSLS. A conventional heuristic rule used to motivate new estimators in this context is approximate bias. This paper formalizes the definition of approximate bias and expands the applicability of approximate bias to various classes of estimators that bridge OLS, TSLS, and Jackknife IV estimators (JIVEs). By evaluating their approximate biases, I propose new approximately unbiased estimators, including UOJIVE1 and…