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Network-Based Clustering for Varying Coefficient Panel Data Models

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In this article, we introduce a novel varying-coefficient panel-data model with locally stationary regressors and unknown group structure, in which the number of groups and the group membership are left unspecified. We develop a triple-localization approach to estimate the unknown subject-specific coefficient functions and then identify the latent group structure via community detection. To improve the efficiency of the first-stage estimator, we further propose a two-stage estimation method that enables the estimator to achieve optimal rates of convergence. In the theoretical part of the article, we derive the asymptotic theory of the resultant estimators. In the empirical part, we present several simulated examples together with an analysis of real data to illustrate the finite-sample performance of the proposed method.

Keywords: Community detection; Group structure; Locally stationary; Two-stage estimation; Varying-coefficient model

Document Type: Research Article

Affiliations: 1: School of Economics, Shandong University, Jinan, China; 2: School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China; 3: Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong

Publication date: April 3, 2022

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