Quantile regression for partially linear varying coefficient spatial autoregressive models

This article considers the quantile regression approach for partially linear spatial autoregressive models with possibly varying coefficients. B-spline is employed for the approximation of varying coefficients. The instrumental variable quantile regression approach is employed for parameter estimati...

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Bibliographic Details
Published inCommunications in statistics. Simulation and computation Vol. 53; no. 9; pp. 4396 - 4411
Main Authors Dai, Xiaowen, Li, Shaoyang, Jin, Libin, Tian, Maozai
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 01.09.2024
Taylor & Francis Ltd
Subjects
Asymptotic methods
Asymptotic properties
Autoregressive models
Hypotheses
Instrumental variables
Monte Carlo simulation
Parameter estimation
Partially linear
Quantile regression
Quantiles
Regression
Spatial autoregressive model
Statistical tests
Varying coefficient
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Summary:This article considers the quantile regression approach for partially linear spatial autoregressive models with possibly varying coefficients. B-spline is employed for the approximation of varying coefficients. The instrumental variable quantile regression approach is employed for parameter estimation. The rank score tests are developed for hypotheses on the coefficients, including the hypotheses on the non-varying coefficients 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.
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ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2022.2154365