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Quantile regression for varying coefficient spatial error models

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  • Xiaowen Dai
  • Erqian Li
  • Maozai Tian

Abstract

This paper investigates the quantile regression estimation for spatial error models with possibly varying coefficients. The local polynomial fitting scheme is employed to approximate the varying coefficients. The rank-based score test is developed for hypotheses on the model and the constancy of the varying coefficients. The asymptotic properties of the proposed estimators and test statistics are both established. Monte Carlo simulations are conducted to study the finite sample performance of the proposed method. Analysis of a real data example is presented for illustration.

Suggested Citation

  • Xiaowen Dai & Erqian Li & Maozai Tian, 2021. "Quantile regression for varying coefficient spatial error models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 50(10), pages 2382-2397, May.
    • Handle: RePEc:taf:lstaxx:v:50:y:2021:i:10:p:2382-2397
    DOI: 10.1080/03610926.2019.1667396
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    Cited by:

    1. Lena Bedawi Elfadli Elmonshid & Omer Ahmed Sayed & Ghadda Mohamed Awad Yousif & Kamal Eldin Hassan Ibrahim Eldaw & Muawya Ahmed Hussein, 2024. "The Impact of Financial Efficiency and Renewable Energy Consumption on CO2 Emission Reduction in GCC Economies: A Panel Data Quantile Regression Approach," Sustainability, MDPI, vol. 16(14), pages 1-16, July.

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