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Model-based Clustering of non-Gaussian Panel Data

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Author Info
Juarez, Miguel A.
Steel, Mark F. J.

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

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Find related papers by JEL classification:
C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis

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  1. 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. [Downloadable!]
    Other versions:
  2. 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.. [Downloadable!] (restricted)
  3. 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. [Downloadable!] (restricted)
  4. Jonathan Temple, 1999. "The New Growth Evidence," Journal of Economic Literature, American Economic Association, vol. 37(1), pages 112-156, March. [Downloadable!] (restricted)
  5. 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.
  6. L. Bauwens & J. V. K. Rombouts, 2007. "Bayesian Clustering of Many Garch Models," Econometric Reviews, Taylor and Francis Journals, vol. 26(2-4), pages 365-386. [Downloadable!] (restricted)
  7. 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. [Downloadable!] (restricted)
  8. 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.
  9. Fabio Canova, 2004. "Testing for Convergence Clubs in Income Per Capita: A Predictive Density Approach," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(1), pages 49-77, 02. [Downloadable!] (restricted)
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  10. 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. [Downloadable!] (restricted)
  11. Liu, Lon-Mu & Tiao, George C., 1980. "Random coefficient first-order autoregressive models," Journal of Econometrics, Elsevier, vol. 13(3), pages 305-325, August. [Downloadable!] (restricted)
  12. Frühwirth-Schnatter, Sylvia & Kaufmann, Sylvia, 2004. "Model-based Clustering of Multiple Time Series," CEPR Discussion Papers 4650, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
    Other versions:
  13. Juarez, Miguel A. & Steel, Mark F. J., 2006. "Non-Gaussian dynamic Bayesian modelling for panel data," MPRA Paper 450, University Library of Munich, Germany. [Downloadable!]
  14. 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. [Downloadable!] (restricted)
  15. Keisuke Hirano, 2002. "Semiparametric Bayesian Inference in Autoregressive Panel Data Models," Econometrica, Econometric Society, vol. 70(2), pages 781-799, March. [Downloadable!] (restricted)
  16. Fernandez, C. & Steel, M.F.J., 1996. "On Bayesian modelling of fat tails and skewness," Discussion Paper 58, Tilburg University, Center for Economic Research. [Downloadable!]
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