GDP clustering: A reappraisal

Michele Battisti, Christopher F. Parmeter

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

This note explores clustering in cross country GDP per capita using recently developed model based clustering methods for panel data. Previous research characterizing the components of the overall distribution of output either use ad hoc methods, or methods which ignore/subvert the panel nature of the data. These new methods allow the characterization of the possible autoregressive relationship of output between time points. We show that traditional static clustering decade by decade gives mixed results regarding clustering over time, while the application of longitudinal mixtures presents three distinct clusters at all periods of time.

Original languageEnglish (US)
Pages (from-to)837-840
Number of pages4
JournalEconomics Letters
Volume117
Issue number3
DOIs
StatePublished - Jan 1 2012

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Keywords

  • Autoregressive
  • Isotropic
  • Mixture densities
  • Output

ASJC Scopus subject areas

  • Finance
  • Economics and Econometrics

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