Semiparametric Estimator for Binary‐outcome Sample Selection: Prejudice Matters in Election
提出一种针对二元结果样本选择模型的半参数估计方法，仅需单指标假设，无需设定误差项分布。通过特殊回归变量变换二元响应，消除选择偏差。应用于2008和2012年美国总统选举数据，评估种族偏见对选举的影响。
Abstract a semiparametric estimator for binary‐outcome sample‐selection models is proposed that imposes only single index assumptions on the selection and outcome equations without specifying the error term distribution. I adopt the idea in Lewbel (2000) using a ‘special regressor’ to transform the binary response Y so that the transformed Y becomes linear in the latent index, which then makes it possible to remove the selection correction term by differencing the transformed Y equation. There are various versions of the estimator, which perform differently trading off bias and variance. A simulation study is conducted, and then I apply the estimators to US presidential election data in 2008 and 2012 to assess the impact of racial prejudice on the elections, as a black candidate was involved for the first time ever in the US history.