Advanced Search
MyIDEAS: Login to save this paper or follow this series

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

Contents:

Author Info

Registered author(s):

    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.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://www.labornrn.at/wp/wp0907.pdf
    Download Restriction: no

    Bibliographic Info

    Paper provided by The Austrian Center for Labor Economics and the Analysis of the Welfare State, Johannes Kepler University Linz, Austria in its series NRN working papers with number 2009-07.

    as in new window
    Length: 42 pages
    Date of creation: Jul 2009
    Date of revision:
    Handle: RePEc:jku:nrnwps:2009_07

    Contact details of provider:
    Postal: NRN Labor Economics and the Welfare State, c/o Rudolf Winter-Ebmer, Altenbergerstr. 69, 4040 Linz
    Phone: +43-732-2468-8216
    Fax: +43-732-2468-8217
    Email:
    Web page: http://www.labornrn.at/
    More information through EDIRC

    Related research

    Keywords: Markov chain Monte Carlo; model-based clustering; panel data; transition matrices; labor market; wage mobility;

    This paper has been announced in the following NEP Reports:

    References

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
    as in new window
    1. Weber, Andrea, 2002. "State Dependence and Wage Dynamics: A Heterogeneous Markov Chain Model for Wage Mobility in Austria," Economics Series 114, Institute for Advanced Studies.
    2. 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.
    3. 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.
    4. Peter Lenk & Wayne DeSarbo, 2000. "Bayesian inference for finite mixtures of generalized linear models with random effects," Psychometrika, Springer, vol. 65(1), pages 93-119, March.
    5. Raferzeder, Thomas & Winter-Ebmer, Rudolf, 2004. "Who is on the Rise in Austria: Wage Mobility and Mobility Risk," IZA Discussion Papers 1329, Institute for the Study of Labor (IZA).
    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 in new window

    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.

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Ren� B�heim).

    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 references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.

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