IDEAS home Printed from https://ideas.repec.org/p/jku/nrnwps/2009_07.html
   My bibliography  Save this paper

Bayesian Clustering of Categorical Time Series Using Finite Mixtures of Markov Chain Models

Author

Abstract

Two approaches for model-based clustering of categorical time series based on time- homogeneous first-order Markov chains are discussed. For Markov chain clustering the in- dividual transition probabilities are fixed to a group-specific transition matrix. In a new approach called Dirichlet multinomial clustering the rows of the individual transition matri- ces deviate from the group mean and follow a Dirichlet distribution with unknown group- specific hyperparameters. Estimation is carried out through Markov chain Monte Carlo. Various well-known clustering criteria are applied to select the number of groups. An appli- cation to a panel of Austrian wage mobility data leads to an interesting segmentation of the Austrian labor market.

Suggested Citation

  • Sylvia Frühwirth-Schnatter & Christoph Pamminger, 2009. "Bayesian Clustering of Categorical Time Series Using Finite Mixtures of Markov Chain Models," NRN working papers 2009-07, The Austrian Center for Labor Economics and the Analysis of the Welfare State, Johannes Kepler University Linz, Austria.
    • Handle: RePEc:jku:nrnwps:2009_07
    as

    Download full text from publisher

    File URL: http://www.labornrn.at/wp/wp0907.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fruhwirth-Schnatter, Sylvia & Fruhwirth, Rudolf, 2007. "Auxiliary mixture sampling with applications to logistic models," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3509-3528, April.
    2. 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, February.
    3. Andrea Weber, 2002. "State dependence and wage dynamics: a heterogeneous Markov chain model for wage mobility in Austria," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 D2-2, International Conferences on Panel Data.
    4. Thomas Raferzeder & Rudolf Winter-Ebmer, 2007. "Who is on the rise in Austria: Wage mobility and mobility risk," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 5(1), pages 39-51, April.
    5. Fraley C. & Raftery A.E., 2002. "Model-Based Clustering, Discriminant Analysis, and Density Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 611-631, June.
    6. Peter Lenk & Wayne DeSarbo, 2000. "Bayesian inference for finite mixtures of generalized linear models with random effects," Psychometrika, Springer;The Psychometric Society, vol. 65(1), pages 93-119, March.
    7. Frydman, Halina, 2005. "Estimation in the Mixture of Markov Chains Moving With Different Speeds," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1046-1053, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. Sylvia Frühwirth-Schnatter, 2011. "Panel data analysis: a survey on model-based clustering of time series," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 5(4), pages 251-280, December.
    3. Sylvia Frühwirth-Schnatter & Stefan Pittner & Andrea Weber & Rudolf Winter-Ebmer, 2016. "Analysing Plant Closure Effects Using Time-Varying Mixture-of-Experts Markov Chain Clustering," CDL Aging, Health, Labor working papers 2016-06, The Christian Doppler (CD) Laboratory Aging, Health, and the Labor Market, Johannes Kepler University Linz, Austria.
    4. 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.
    5. Fruhwirth-Schnatter, Sylvia & Fruhwirth, Rudolf, 2007. "Auxiliary mixture sampling with applications to logistic models," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3509-3528, April.
    6. Juarez, Miguel A. & Steel, Mark F. J., 2006. "Model-based Clustering of non-Gaussian Panel Data," MPRA Paper 880, University Library of Munich, Germany.
    7. Hyowon Kim & Dong Soo Kim & Greg M. Allenby, 2020. "Benefit Formation and Enhancement," Quantitative Marketing and Economics (QME), Springer, vol. 18(4), pages 419-468, December.
    8. Mihály Borsi & Norbert Metiu, 2015. "The evolution of economic convergence in the European Union," Empirical Economics, Springer, vol. 48(2), pages 657-681, March.
    9. Robert Zeithammer & Peter Lenk, 2006. "Bayesian estimation of multivariate-normal models when dimensions are absent," Quantitative Marketing and Economics (QME), Springer, vol. 4(3), pages 241-265, September.
    10. Massimiliano Affinito, 2011. "Convergence clubs, the euro-area rank and the relationship between banking and real convergence," Temi di discussione (Economic working papers) 809, Bank of Italy, Economic Research and International Relations Area.
    11. Ronald Bachmann & Peggy Bechara & Sandra Schaffner, 2016. "Wage Inequality and Wage Mobility in Europe," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 62(1), pages 181-197, March.
    12. Durlauf, Steven N., 2001. "Manifesto for a growth econometrics," Journal of Econometrics, Elsevier, vol. 100(1), pages 65-69, January.
    13. Stilianos Alexiadis & Matthias Koch & Tamás Krisztin, 2011. "Time series and spatial interaction: An alternative method to detect converging clusters," ERSA conference papers ersa11p1678, European Regional Science Association.
    14. Cristina Brasili & Luciano Gutierrez, 2004. "Regional convergence across European Union," Development and Comp Systems 0402002, University Library of Munich, Germany.
    15. Steven N. Durlauf & Andros Kourtellos & Chih Ming Tan, 2008. "Empirics of Growth and Development," Chapters, in: Amitava Krishna Dutt & Jaime Ros (ed.), International Handbook of Development Economics, Volumes 1 & 2, volume 0, chapter 3, Edward Elgar Publishing.
    16. Igari, Ryosuke & Hoshino, Takahiro, 2018. "A Bayesian data combination approach for repeated durations under unobserved missing indicators: Application to interpurchase-timing in marketing," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 150-166.
    17. Barrios, Salvador & Strobl, Eric, 2009. "The dynamics of regional inequalities," Regional Science and Urban Economics, Elsevier, vol. 39(5), pages 575-591, September.
    18. Pourahmadi, Mohsen & Daniels, Michael J. & Park, Trevor, 2007. "Simultaneous modelling of the Cholesky decomposition of several covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 98(3), pages 568-587, March.
    19. Castellacci, Fulvio, 2008. "Technology clubs, technology gaps and growth trajectories," Structural Change and Economic Dynamics, Elsevier, vol. 19(4), pages 301-314, December.
    20. Trueck, Stefan & Rachev, Svetlozar T., 2008. "Rating Based Modeling of Credit Risk," Elsevier Monographs, Elsevier, edition 1, number 9780123736833.

    More about this item

    Keywords

    Markov chain Monte Carlo; model-based clustering; panel data; transition matrices; labor market; wage mobility;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jku:nrnwps:2009_07. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: René Böheim (email available below). General contact details of provider: https://edirc.repec.org/data/aclawat.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.