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Minimum distance quantile regression for spatial autoregressive panel data models with fixed effects

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  • Xiaowen Dai
  • Libin Jin

Abstract

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.

Suggested Citation

  • Xiaowen Dai & Libin Jin, 2021. "Minimum distance quantile regression for spatial autoregressive panel data models with fixed effects," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-13, December.
    • Handle: RePEc:plo:pone00:0261144
    DOI: 10.1371/journal.pone.0261144
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    References listed on IDEAS

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