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GDP clustering: A reappraisal

  • Battisti, Michele
  • Parmeter, Christopher F.

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.

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Article provided by Elsevier in its journal Economics Letters.

Volume (Year): 117 (2012)
Issue (Month): 3 ()
Pages: 837-840

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Handle: RePEc:eee:ecolet:v:117:y:2012:i:3:p:837-840
Contact details of provider: Web page: http://www.elsevier.com/locate/ecolet

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  1. Danny Quah, 1992. "Empirical Cross-Section Dynamics in Economic Growth," FMG Discussion Papers dp154, Financial Markets Group.
  2. Michele Battisti & Christopher F. Parmeter, 2011. "Income Polarization, Convergence Tools and Mixture Analysis," Working Papers 2011-17, University of Miami, Department of Economics.
  3. Maria Grazia Pittau & Roberto Zelli & Paul A. Johnson, 2010. "Mixture Models, Convergence Clubs, And Polarization," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 56(1), pages 102-122, 03.
  4. Fraley C. & Raftery A.E., 2002. "Model-Based Clustering, Discriminant Analysis, and Density Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 611-631, June.
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