Common correlated effects estimation of heterogeneous dynamic panel quantile regression models
提出了一种针对含滞后因变量和交互效应的异质性面板模型的分位数回归估计方法，采用共同相关效应（CCE）方法，证明了估计量的一致性并推导了渐近分布，蒙特卡洛研究表明当时间维度较大时该估计量优于现有方法，并应用于分时定价评估。
Summary This paper proposes a quantile regression estimator for a heterogeneous panel model with lagged dependent variables and interactive effects. The paper adopts the Common Correlated Effects (CCE) approach proposed in the literature and demonstrates that the extension to the estimation of dynamic quantile regression models is feasible under similar conditions to the ones used in the literature. The new quantile regression estimator is shown to be consistent and its asymptotic distribution is derived. Monte Carlo studies are carried out to study the small sample behavior of the proposed approach. The evidence shows that the estimator can significantly improve on the performance of existing estimators as long as the time series dimension of the panel is large. We present an application to the evaluation of Time‐of‐Use pricing using a large randomized control trial.