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Model-based Clustering of non-Gaussian Panel Data

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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

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File URL: http://mpra.ub.uni-muenchen.de/880/
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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 880.

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Date of creation: 20 Nov 2006
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Handle: RePEc:pra:mprapa:880

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

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References

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  1. Nandram, Balgobin & Petruccelli, Joseph D, 1997. "A Bayesian Analysis of Autoregressive Time Series Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 328-34, July.
  2. Canova, Fabio, 2001. "Testing for convergence clubs in income per-capita : a predictive density approach," HWWA Discussion Papers 139, Hamburg Institute of International Economics (HWWA).
  3. Ishwaran H. & James L.F. & Sun J., 2001. "Bayesian Model Selection in Finite Mixtures by Marginal Density Decompositions," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1316-1332, December.
  4. 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.
  5. Nerlove,Marc, 2005. "Essays in Panel Data Econometrics," Cambridge Books, Cambridge University Press, number 9780521022460, April.
  6. 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.
  7. Fruhwirth-Schnatter, Sylvia & Kaufmann, Sylvia, 2008. "Model-Based Clustering of Multiple Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 78-89, January.
  8. BAUWENS, Luc & ROMBOUTS, Jeroen VK, . "Bayesian clustering of many GARCH models," CORE Discussion Papers RP -1916, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  9. Quah, Danny, 1997. "Empirics for Growth and Distribution: Stratification, Polarization, and Convergence Clubs," CEPR Discussion Papers 1586, C.E.P.R. Discussion Papers.
  10. Jonathan Temple, 1999. "The New Growth Evidence," Journal of Economic Literature, American Economic Association, vol. 37(1), pages 112-156, March.
  11. Juarez, Miguel A. & Steel, Mark F. J., 2006. "Non-Gaussian dynamic Bayesian modelling for panel data," MPRA Paper 450, University Library of Munich, Germany.
  12. 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 365-84, Oct.-Dec..
  13. Marin, Jean-Michel & Mengersen, Kerrie & Robert, Christian P., 2005. "Bayesian Modelling and Inference on Mixtures of Distributions," Economics Papers from University Paris Dauphine 123456789/6069, Paris Dauphine University.
  14. Alonso-Borrego, Cesar & Arellano, Manuel, 1999. "Symmetrically Normalized Instrumental-Variable Estimation Using Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 36-49, January.
  15. Liu, Lon-Mu & Tiao, George C., 1980. "Random coefficient first-order autoregressive models," Journal of Econometrics, Elsevier, vol. 13(3), pages 305-325, August.
  16. Carmen Fernandez & Mark F. J. Steel, 2004. "Bayesian Regression Analysis with scale mixtures of normals," ESE Discussion Papers 27, Edinburgh School of Economics, University of Edinburgh.
  17. 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.
  18. Fernández, C. & Steel, M.F.J., 1996. "On Bayesian Modelling of Fat Tails and Skewness," Discussion Paper 1996-58, Tilburg University, Center for Economic Research.
  19. Sylvia Fruhwirth-Schnatter, 2004. "Estimating marginal likelihoods for mixture and Markov switching models using bridge sampling techniques," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 143-167, 06.
  20. Keisuke Hirano, 2002. "Semiparametric Bayesian Inference in Autoregressive Panel Data Models," Econometrica, Econometric Society, vol. 70(2), pages 781-799, March.
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Cited by:
  1. Sylvia Frühwirth-Schnatter & Andrea Weber & Rudolf Winter-Ebmer, 2010. "Labor Market Entry and Earnings Dynamics: Bayesian Inference Using Mixtures-of-Experts Markov Chain Clustering," Economics working papers 2010-11, Department of Economics, Johannes Kepler University Linz, Austria.
  2. Geert Mesters & Siem Jan Koopman, 2012. "Generalized Dynamic Panel Data Models with Random Effects for Cross-Section and Time," Tinbergen Institute Discussion Papers 12-009/4, Tinbergen Institute, revised 18 Mar 2014.

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