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Estimating Transition Probabilities from a Time Series of Independent Cross Sections

Author

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  • Ben Pelzer
  • Rob Eisinga
  • Philip Hans Franses

Abstract

This paper considers the implementation of a nonstationary, heterogeneous Markov model for the analysis of a binary dependent variable in a time series of independent cross sections. The model, previously considered by Moffitt (1993), offers the opportunity to estimate entry and exit transition probabilities and to examine the effects of time‐constant and time‐varying covariates on the hazards. We show how ML estimates of the parameters can be obtained by Fisher's method‐of‐scoring and how to estimate both fixed and time‐varying covariate effects. The model is exemplified with an analysis of the labor force participation decision of Dutch women using data from the Socio‐economic Panel (SEP) study conducted in the Netherlands between 1986 and 1995. We treat the panel data as independent cross sections and compare the employment status sequences predicted by the model with the observed sequences in the panel. Some open problems concerning the application of the model are also discussed.

Suggested Citation

  • Ben Pelzer & Rob Eisinga & Philip Hans Franses, 2001. "Estimating Transition Probabilities from a Time Series of Independent Cross Sections," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 55(2), pages 249-262, July.
    • Handle: RePEc:bla:stanee:v:55:y:2001:i:2:p:249-262
    DOI: 10.1111/1467-9574.00168
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    Cited by:

    1. Boualem Rabta & Bart van den Boom & Vasco Molini, 2016. "A Continuous†time Markov Chain Approach for Modeling of Poverty Dynamics: Application to Mozambique," African Development Review, African Development Bank, vol. 28(4), pages 482-495, December.
    2. Arie ten Cate, 2014. "Maximum likelihood estimation of the Markov chain model with macro data and the ecological inference model," CPB Discussion Paper 284, CPB Netherlands Bureau for Economic Policy Analysis.
    3. José Carlos Ramírez & Francisco Ortiz-Arango & Leovardo Mata, 2021. "The Markovian Pattern of Social Deprivation for Mexicans with Diabetes," Mathematics, MDPI, vol. 9(7), pages 1-17, April.
    4. Arie ten Cate, 2014. "Maximum likelihood estimation of the Markov chain model with macro data and the ecological inference model," CPB Discussion Paper 284.rdf, CPB Netherlands Bureau for Economic Policy Analysis.

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