Quantile Selection Models With an Application to Understanding Changes in Wage Inequality
提出一种纠正分位数回归中样本选择偏差的方法，通过估计参与决策与结果方程误差的联合分布来调整分位数水平，并应用于1978-2000年英国数据以纠正工资分位数中的就业选择偏差。
We propose a method to correct for sample selection in quantile regression models. Selection is modelled via the cumulative distribution function, or copula, of the percentile error in the outcome equation and the error in the participation decision. Copula parameters are estimated by minimizing a method-of-moments criterion. Given these parameter estimates, the percentile levels of the outcome are re-adjusted to correct for selection, and quantile parameters are estimated by minimizing a rotated “check” function. We apply the method to correct wage percentiles for selection into employment, using data for the UK for the period 1978-2000. We also extend the method to account for the presence of equilibrium e?ects when performing counterfactual exercises.