School of Statistics and Mathematics, Shanghai Lixin University of Accounting and Finance, Shanghai, China.
Interdisciplinary Research Institute of Data Science, Shanghai Lixin University of Accounting and Finance, Shanghai, China.
PLoS One. 2021 Dec 14;16(12):e0261144. doi: 10.1371/journal.pone.0261144. eCollection 2021.
This paper considers the quantile regression model with individual fixed effects for spatial panel data. Efficient minimum distance quantile regression estimators based on instrumental variable (IV) method are proposed for parameter estimation. The proposed estimator is computational fast compared with the IV-FEQR estimator proposed by Dai et al. (2020). Asymptotic properties of the proposed estimators are also established. Simulations are conducted to study the performance of the proposed method. Finally, we illustrate our methodologies using a cigarettes demand data set.
本文考虑了具有个体固定效应的空间面板数据的分位数回归模型。针对参数估计，提出了一种基于工具变量（IV）方法的有效最小距离分位数回归估计量。与 Dai 等人（2020）提出的 IV-FEQR 估计量相比，该估计量的计算速度更快。还建立了所提出估计量的渐近性质。通过模拟研究了所提出方法的性能。最后，我们使用香烟需求数据集来说明我们的方法。
