Instrumental Variable Quantile Regression of Spatial Dynamic Durbin Panel Data Model with Fixed Effects

Danqing Chen, Jianbao Chen and Shuangshuang Li
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Danqing Chen: College of Mathematics and Statistics, Fujian Normal University, Fuzhou 350117, China
Jianbao Chen: College of Mathematics and Statistics, Fujian Normal University, Fuzhou 350117, China
Shuangshuang Li: College of Mathematics and Statistics, Fujian Normal University, Fuzhou 350117, China

Mathematics, 2021, vol. 9, issue 24, 1-24

Abstract: This paper studies a quantile regression spatial dynamic Durbin panel data (SDDPD) model with fixed effects. Conventional fixed effects estimators of quantile regression specification are usually biased in the presentation of lagged response variables in spatial and time as regressors. To reduce this bias, we propose the instrumental variable quantile regression (IVQR) estimator with lagged covariates in spatial and time as instruments. Under some regular conditions, the consistency and asymptotic normalityof the estimators are derived. Monte Carlo simulations show that our estimators not only perform well in finite sample cases at different quantiles but also have robustness for different spatial weights matrices and for different disturbance term distributions. The proposed method is used to analyze the influencing factors of international tourism foreign exchange earnings of 31 provinces in China from 2011 to 2017.

Keywords: SDDPD model; IVQR; large sample property; Monte Carlo simulation; international tourism foreign exchange earnings (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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