Quantile-regression-based clustering for panel data

Yingying Zhang, Huixia Judy Wang, Zhongyi Zhu

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

In panel data analysis, it is important to identify subgroups of units with heterogeneous parameters. This can not only increase the model flexibility but also produce more efficient estimation by pooling information across units within the same group. In this paper, we propose a new quantile-regression-based clustering method for panel data. We develop an iterative algorithm using a similar idea of k-means clustering to identify subgroups with heterogeneous slopes at a single quantile level or across multiple quantiles. The asymptotic properties of the group membership estimator and corresponding group-specific slope estimator are established. The finite sample performance of the proposed method is assessed through simulation and the analysis of an economic growth data.
Original languageEnglish (US)
Pages (from-to)54-67
Number of pages14
JournalJournal of Econometrics
Volume213
Issue number1
DOIs
StatePublished - Nov 2019
Externally publishedYes

ASJC Scopus subject areas

  • History and Philosophy of Science
  • Economics and Econometrics
  • Applied Mathematics

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