GDP clustering: A reappraisal
AbstractThis 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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Bibliographic InfoArticle provided by Elsevier in its journal Economics Letters.
Volume (Year): 117 (2012)
Issue (Month): 3 ()
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Web page: http://www.elsevier.com/locate/ecolet
Mixture densities; Output; Autoregressive; Isotropic;
Find related papers by JEL classification:
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
- O15 - Economic Development, Technological Change, and Growth - - Economic Development - - - Economic Development: Human Resources; Human Development; Income Distribution; Migration
- O16 - Economic Development, Technological Change, and Growth - - Economic Development - - - Financial Markets; Saving and Capital Investment; Corporate Finance and Governance
- O47 - Economic Development, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Measurement of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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