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Inferring transition probabilities from repeated cross sections: a cross-level inference approach to US presidential voting

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  • Pelzer, B.
  • Eisinga, R.
  • Franses, Ph.H.B.F.

Abstract

This paper outlines a nonstationary, heterogeneous Markov model designed to estimate entry and exit transition probabilities at the micro-level from a time series of independent cross-sectional samples with a binary outcome variable. The model has its origins in the work of Moffitt (1993) and shares features with standard statistical methods for ecological inference. We show how ML estimates of the parameters can be obtained by the method-of- scoring, how to estimate time-varying covariate effects, and how to include non-backcastable variables in the model. The latter extension of the basic model is an important one as it strongly increases its potential application in a wide array of research contexts. The example illustration uses survey data on American presidential vote intentions from a five-wave panel study conducted by Patterson (1980) in 1976. We treat the panel data as independent cross sections and compare the estimates of the Markov model with the observations in the panel. Directions for future work are discussed.

Suggested Citation

  • 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.
  • Handle: RePEc:ems:eureir:1687
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    File URL: https://repub.eur.nl/pub/1687/feweco20010808163622.pdf
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    References listed on IDEAS

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    1. Amemiya, Takeshi, 1981. "Qualitative Response Models: A Survey," Journal of Economic Literature, American Economic Association, vol. 19(4), pages 1483-1536, December.
    2. R. L. Chambers & D. G. Steel, 2001. "Simple methods for ecological inference in 2×2 tables," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 175-192.
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    Keywords

    Markov model; transition probabilities;

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