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

Author

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  • Danqing Chen

    (College of Mathematics and Statistics, Fujian Normal University, Fuzhou 350117, China
    School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, 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)

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.

Suggested Citation

  • Danqing Chen & Jianbao Chen & Shuangshuang Li, 2021. "Instrumental Variable Quantile Regression of Spatial Dynamic Durbin Panel Data Model with Fixed Effects," Mathematics, MDPI, vol. 9(24), pages 1-24, December.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:24:p:3261-:d:703487
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    References listed on IDEAS

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