Rank-based instrumental variable estimation for semiparametric varying coefficient spatial autoregressive models

Yangbing Tang, Zhongzhan Zhang and Jiang Du ()
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Yangbing Tang: Beijing University of Technology
Zhongzhan Zhang: Beijing University of Technology
Jiang Du: Beijing University of Technology

Statistical Papers, 2024, vol. 65, issue 3, No 24, 1805-1839

Abstract: Abstract In this paper, it is aim to propose an instrumental variable rank estimation method for varying coefficient spatial autoregressive models. The newly proposed method provides a highly efficient and robust alternative to the existing quasi-maximum likelihood estimation or GMM estimation, and can be implemented using the existing R software package conveniently. Under mild conditions, the consistency and asymptotic normality of the resulting estimators are established. The finite sample properties of the proposed method are investigated through Monte Carlo simulation studies. Finally, the Boston house price data and crime data of Tokyo are analyzed to illustrate the usefulness of the proposed estimation method.

Keywords: Rank estimation; Instrumental variable; Varying-coefficient; Spatial autoregressive model; 62J05; 62M10 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00362-023-01466-5

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