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Bayesian estimation of transition probabilities from repeated cross sections

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  • Ben Pelzer
  • Rob Eisinga

Abstract

This paper discusses some simple practical advantages of Markov chain Monte Carlo (MCMC) methods in estimating entry and exit transition probabilities from repeated independent surveys. Simulated data are used to illustrate the usefulness of MCMC methods when the likelihood function has multiple local maxima. Actual data on the evaluation of an HIV prevention intervention program among drug users are used to demonstrate the advantage of using prior information to enhance parameter identificaiton. The latter example also demonstrates an important strength of the MCMC approach, namely the ability to make inferences on arbitrary functions of model parameters.

Suggested Citation

  • Ben Pelzer & Rob Eisinga, 2002. "Bayesian estimation of transition probabilities from repeated cross sections," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 56(1), pages 23-33, February.
    • Handle: RePEc:bla:stanee:v:56:y:2002:i:1:p:23-33
    DOI: 10.1111/1467-9574.00063
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    1. Pelzer, B. & Eisinga, R. & Franses, Ph.H.B.F., 2001. "Inferring transition probabilities from repeated cross sections: a cross-level inference approach to US presidential voting," Econometric Institute Research Papers EI 2001-21, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
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    Cited by:

    1. Hugo Storm & Thomas Heckelei & Ron C. Mittelhammer, 2016. "Bayesian estimation of non-stationary Markov models combining micro and macro data," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 43(2), pages 303-329.
    2. William Lim & Gaurav Khemka & David Pitt & Bridget Browne, 2019. "A method for calculating the implied no-recovery three-state transition matrix using observable population mortality incidence and disability prevalence rates among the elderly," Journal of Population Research, Springer, vol. 36(3), pages 245-282, September.

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