论文标题
分位数回归估计器的偏差校正
Bias correction for quantile regression estimators
论文作者
论文摘要
我们研究了经典分位数回归和仪器可变回归估计值的偏见。虽然是渐近的一阶无偏见，但这些估计量可能具有不可忽略的二阶偏见。我们使用经验过程理论得出了这些估计量的高阶随机扩展。基于此扩展，我们得出了二阶偏差的明确公式，并提出了使用偏置组件的有限差异估计量的可行偏差校正程序。所提出的偏置校正方法在模拟中表现良好。我们使用恩格尔（Engel）的家庭食品支出的经典数据提供了经验插图。
We study the bias of classical quantile regression and instrumental variable quantile regression estimators. While being asymptotically first-order unbiased, these estimators can have non-negligible second-order biases. We derive a higher-order stochastic expansion of these estimators using empirical process theory. Based on this expansion, we derive an explicit formula for the second-order bias and propose a feasible bias correction procedure that uses finite-difference estimators of the bias components. The proposed bias correction method performs well in simulations. We provide an empirical illustration using Engel's classical data on household food expenditure.
