Regression clustering for panel-data models with fixed effects

Demetris Christodoulou, Vasilis Sarafidis

Research output: Contribution to journalArticleResearchpeer-review

12 Citations (Scopus)

Abstract

In this article, we describe the xtregcluster command, which implements the panel regression clustering approach developed by Sarafidis and Weber (2015, Oxford Bulletin of Economics and Statistics 77: 274–296). The method classifies individuals into clusters, so that within each cluster, the slope parameters are homogeneous and all intracluster heterogeneity is due to the standard two-way error-components structure. Because the clusters are heterogeneous, they do not share common parameters. The number of clusters and the optimal partition are determined by the clustering solution, which minimizes the total residual sum of squares of the model subject to a penalty function that strictly increases in the number of clusters. The method is available for linear short panel-data models and useful for exploring heterogeneity in the slope parameters when there is no a priori knowledge about parameter structures. It is also useful for empirically evaluating whether any normative classifications are justifiable from a statistical point of view.

Original languageEnglish
Article numberst0475
Pages (from-to)314-329
Number of pages16
JournalThe Stata Journal
Volume17
Issue number2
Publication statusPublished - 2017

Keywords

  • Panel data
  • Parameter heterogeneity
  • st0475
  • Xtregcluster

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