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Testing for Convergence Clubs in Income Per Capita: A Predictive Density Approach

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  • Fabio Canova

Abstract

The article proposes a technique, based on the predictive density of the data, conditional on the parameters of the model, to jointly tests for groups of unknown size in a panel and to estimate the parameters of each group. The procedure is applied to the problem of identifying convergence clubs in scaled income per capita data. The steady-state distribution of European regional data clusters around four poles of attraction with different economic features. The distribution of income per capita of OECD countries has two poles of attraction and each group clearly identifiable economic characteristics. Copyright 2004 by the Economics Department Of The University Of Pennsylvania And Osaka University Institute Of Social And Economic Research Association.

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Bibliographic Info

Article provided by Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association in its journal International Economic Review.

Volume (Year): 45 (2004)
Issue (Month): 1 (02)
Pages: 49-77

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Handle: RePEc:ier:iecrev:v:45:y:2004:i:1:p:49-77

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