This paper considers the estimation of panel data models with interactive fixed effects where the idiosyncratic errors are subject to conditional quantile restrictions. An easy-to-implement two-step estimator is proposed for the coefficients of the observed regressors. In the first step, the principal component analysis is applied to the cross-sectional averages of the regressors to estimate the latent factors. In the second step, the smoothed quantile regression is used to estimate the coefficients of the observed regressors and the factor loadings jointly. The consistency and asymptotic normality of the estimator are established under large $N,T$ asymptotics. It is found that the asymptotic distribution of the estimator suffers from asymptotic biases, and this paper shows how to correct the biases using both analytical and split-panel jackknife bias corrections. Simulation studies confirm that the proposed estimator performs well with moderate sample sizes.

本文考虑具有交互固定效应的面板数据模型的估计，其中特殊误差受到条件分位数限制。针对观察到的回归量的系数提出了一种易于实现的两步估计器。第一步，将主成分分析应用于回归量的横截面平均值以估计潜在因子。在第二步中，使用平滑分位数回归来联合估计观察到的回归量和因子载荷的系数。估计量的一致性和渐近正态性是在大$N,T$渐近下建立的。我们发现估计量的渐近分布存在渐近偏差，本文展示了如何使用解析偏差校正和分割面板折刀偏差校正来校正偏差。模拟研究证实，所提出的估计器在适度的样本量下表现良好。