A Test of the Conditional Independence Assumption in Sample Selection Models
研究了样本选择模型中条件独立性假设的检验方法，提出基于条件分位数回归过程的Kolmogorov-Smirnov型检验，蒙特卡洛模拟显示检验效果良好，并应用于2011年当前人口调查的女性工资数据，发现同质性被显著拒绝。
Identification in most sample selection models depends on the independence of the regressors and the error terms conditional on the selection probability. All quantile and mean functions are parallel in these models; this implies that quantile estimators cannot reveal any—per assumption non-existing—heterogeneity. Quantile estimators are nevertheless useful for testing the conditional independence assumption because they are consistent under the null hypothesis. We propose tests of the Kolmogorov–Smirnov type based on the conditional quantile regression process. Monte Carlo simulations show that their size is satisfactory and their power sufficient to detect deviations under plausible data-generating processes. We apply our procedures to female wage data from the 2011 Current Population Survey and show that homogeneity is clearly rejected. Copyright © 2015 John Wiley & Sons, Ltd.