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Model-based Clustering of non-Gaussian Panel Data

Author

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  • Juarez, Miguel A.
  • Steel, Mark F. J.

Abstract

In this paper we propose a model-based method to cluster units within a panel. The underlying model is autoregressive and non-Gaussian, allowing for both skewness and fat tails, and the units are clustered according to their dynamic behaviour and equilibrium level. Inference is addressed from a Bayesian perspective and model comparison is conducted using the formal tool of Bayes factors. Particular attention is paid to prior elicitation and posterior propriety. We suggest priors that require little subjective input from the user and possess hierarchical structures that enhance the robustness of the inference. Two examples illustrate the methodology: one analyses economic growth of OECD countries and the second one investigates employment growth of Spanish manufacturing firms

Suggested Citation

  • Juarez, Miguel A. & Steel, Mark F. J., 2006. "Model-based Clustering of non-Gaussian Panel Data," MPRA Paper 880, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:880
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    File URL: https://mpra.ub.uni-muenchen.de/880/1/MPRA_paper_880.pdf
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    References listed on IDEAS

    as
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    3. Durlauf, Steven N & Johnson, Paul A, 1995. "Multiple Regimes and Cross-Country Growth Behaviour," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(4), pages 365-384, Oct.-Dec..
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    5. G. Casella & K. L. Mengersen & C. P. Robert & D. M. Titterington, 2002. "Perfect samplers for mixtures of distributions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 777-790.
    6. Miguel A. Juárez & Mark F. J. Steel, 2010. "Non‐gaussian dynamic bayesian modelling for panel data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(7), pages 1128-1154, November/.
    7. Keisuke Hirano, 2002. "Semiparametric Bayesian Inference in Autoregressive Panel Data Models," Econometrica, Econometric Society, vol. 70(2), pages 781-799, March.
    8. Quah, Danny, 1997. "Empirics for Growth and Distribution: Stratification, Polarization, and Convergence Clubs," CEPR Discussion Papers 1586, C.E.P.R. Discussion Papers.
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    10. Durlauf, Steven N. & Quah, Danny T., 1999. "The new empirics of economic growth," Handbook of Macroeconomics,in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 4, pages 235-308 Elsevier.
    11. Matthew Stephens, 2000. "Dealing with label switching in mixture models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 795-809.
    12. Fern ndez, Carmen & Steel, Mark F.J., 2000. "Bayesian Regression Analysis With Scale Mixtures Of Normals," Econometric Theory, Cambridge University Press, vol. 16(01), pages 80-101, February.
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    14. Nerlove,Marc, 2005. "Essays in Panel Data Econometrics," Cambridge Books, Cambridge University Press, number 9780521022460.
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    Citations

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    Cited by:

    1. Sylvia Frühwirth‐Schnatter & Christoph Pamminger & Andrea Weber & Rudolf Winter‐Ebmer, 2012. "Labor market entry and earnings dynamics: Bayesian inference using mixtures‐of‐experts Markov chain clustering," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(7), pages 1116-1137, November.
    2. Aßmann, Christian & Boysen-Hogrefe, Jens, 2011. "A Bayesian approach to model-based clustering for binary panel probit models," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 261-279, January.

    More about this item

    Keywords

    autoregressive modelling; employment growth; GDP growth convergence; hierarchical prior; model comparison; posterior propriety; skewness;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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