Network-Based Clustering for Varying Coefficient Panel Data Models

Youquan Pei, Tao Huang, Heng PENG, Jinhong You*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
JournalJournal of Business and Economic Statistics
DOIs
Publication statusAccepted/In press - 2020

Scopus Subject Areas

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

User-Defined Keywords

  • Community detection
  • Group structure
  • Locally stationary
  • Two-stage estimation
  • Varying-coefficient model

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