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Quantile regression for general spatial panel data models with fixed effects

Author

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  • Xiaowen Dai
  • Zhen Yan
  • Maozai Tian
  • ManLai Tang

Abstract

This paper considers the quantile regression model with both individual fixed effect and time period effect for general spatial panel data. Fixed effects quantile regression estimators based on instrumental variable method will be proposed. Asymptotic properties of the proposed estimators will be developed. Simulations are conducted to study the performance of the proposed method. We will illustrate our methodologies using a cigarettes demand data set.

Suggested Citation

  • Xiaowen Dai & Zhen Yan & Maozai Tian & ManLai Tang, 2020. "Quantile regression for general spatial panel data models with fixed effects," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(1), pages 45-60, January.
    • Handle: RePEc:taf:japsta:v:47:y:2020:i:1:p:45-60
    DOI: 10.1080/02664763.2019.1628190
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    Cited by:

    1. 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.
    2. Xiaowen Dai & Shidan Huang & Libin Jin & Maozai Tian, 2023. "Wild Bootstrap-Based Bias Correction for Spatial Quantile Panel Data Models with Varying Coefficients," Mathematics, MDPI, vol. 11(9), pages 1-16, April.

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