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Evidence of Convergence Clubs Using Mixture Models

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  • Maria Grazia Pittau
  • Roberto Zelli
  • Riccardo Massari

Abstract

Cross-country economic convergence has been increasingly investigated by finite mixture models. Multiple components in a mixture reflect groups of countries that converge locally. Testing for the number of components is crucial for detecting “convergence clubs.” To assess the number of components of the mixture, we propose a sequential procedure that compares the shape of the hypothesized mixture distribution with the true unknown density, consistently estimated through a kernel estimator. The novelty of our approach is its capability to select the number of components along with a satisfactory fitting of the model. Simulation studies and an empirical application to per capita income distribution across countries testify for the good performance of our approach. A three-clubs convergence seems to emerge.

Suggested Citation

  • Maria Grazia Pittau & Roberto Zelli & Riccardo Massari, 2016. "Evidence of Convergence Clubs Using Mixture Models," Econometric Reviews, Taylor & Francis Journals, vol. 35(7), pages 1317-1342, August.
    • Handle: RePEc:taf:emetrv:v:35:y:2016:i:7:p:1317-1342
    DOI: 10.1080/07474938.2014.977062
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    References listed on IDEAS

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    1. G. J. McLachlan, 1987. "On Bootstrapping the Likelihood Ratio Test Statistic for the Number of Components in a Normal Mixture," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(3), pages 318-324, November.
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    Cited by:

    1. Paul Johnson & Chris Papageorgiou, 2020. "What Remains of Cross-Country Convergence?," Journal of Economic Literature, American Economic Association, vol. 58(1), pages 129-175, March.
    2. Maria Grazia Pittau & Roberto Zelli, 2017. "At the roots of Gini’s transvariation: extracts from “Il concetto di transvariazione e le sue prime applicazioni”," METRON, Springer;Sapienza Università di Roma, vol. 75(2), pages 127-140, August.
    3. Mendez-Guerra, Carlos, 2017. "Convergence Clubs Beyond GDP: A Non-Parametric Density Approach," MPRA Paper 82048, University Library of Munich, Germany.
    4. Mendez, Carlos, 2019. "Regional Efficiency Dispersion, Convergence, and Efficiency Clusters: Evidence from the Provinces of Indonesia 1990-2010," MPRA Paper 95972, University Library of Munich, Germany.
    5. Yan, Cheng & Cheng, Tingting, 2019. "In search of the optimal number of fund subgroups," Journal of Empirical Finance, Elsevier, vol. 50(C), pages 78-92.
    6. Carlos Mendez, 2019. "Lack of Global Convergence and the Formation of Multiple Welfare Clubs across Countries: An Unsupervised Machine Learning Approach," Economies, MDPI, vol. 7(3), pages 1-17, July.
    7. Carlos Mendez, 2020. "Regional efficiency convergence and efficiency clusters," Asia-Pacific Journal of Regional Science, Springer, vol. 4(2), pages 391-411, June.
    8. Nartikoev, Alan & Peresetsky, Anatoly, 2020. "Эндогенная Классификация Домохозяйств В Регионах России [Endogenous household classification: Russian regions]," MPRA Paper 104351, University Library of Munich, Germany.

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