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A Penalized Profile Quasi-Likelihood Method for a Semiparametric Varying Coefficient Spatial Autoregressive Panel Model with Fixed Effects

Year: 2025

Type: article

Abstract: This paper proposes a variable selection method for a semiparametric varying coefficient spatial autoregressive panel model with fixed effects based on a penalized profile quasi-likelihood method, which can simultaneously select significant variables in parametric components and nonparametric components without estimating fixed effects. With an appropriate selection of the tuning parameters and some mild assumptions, the consistency of this procedure and the oracle property of the obtained estimators are established. Then, we conduct some Monte Carlo simulations to assess the finite sample performance of the proposed variable selection method, and finally, we analyze a real dataset for further illustration.

Source: Axioms

DOI: 10.3390/axioms14020121

Authors: Ruiqin Tian, Miaojie Xia, Dengke Xu

Institutions: Hangzhou Normal University, Beijing University of Technology, Hangzhou Dianzi University

Language: English

Topic: Spatial and Panel Data Analysis

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funders: National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province