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Latent class models for ecological inference on voters transitions

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  • Roberto Colombi

    (University of Bergamo)

  • Antonio Forcina

    (University of Perugia)

Abstract

This paper introduces some new models of ecological inference within the context of estimation of voter transitions across elections. In particular, we assume that voters of a given party in a given occasion may be split into two latent types: faithful voters, who will certainly vote again for the same party and movers, who will reconsider their choice. Our models allow for unobserved heterogeneity across polling stations both in the weights of the two latent classes within each party and also when modelling the choice of unfaithful voters. Different ways of modelling the unobserved heterogeneity are considered by exploiting properties of the Dirichlet-multinomial distribution and the Brown Payne model of voting transitions can be seen as a special case within the class of models presented here. We discuss pseudo-maximum likelihood estimation and present an application to recent elections in Italy.

Suggested Citation

  • Roberto Colombi & Antonio Forcina, 2016. "Latent class models for ecological inference on voters transitions," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(4), pages 501-517, November.
    • Handle: RePEc:spr:stmapp:v:25:y:2016:i:4:d:10.1007_s10260-015-0349-0
    DOI: 10.1007/s10260-015-0349-0
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    References listed on IDEAS

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    1. King, Gary, 2004. "EI: A Program for Ecological Inference," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i07).
    2. N. Cleave & P. J. Brown & C. D. Payne, 1995. "Evaluation of Methods for Ecological Inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 158(1), pages 55-72, January.
    3. David E. Campbell & John C. Green & Geoffrey C. Layman, 2011. "The Party Faithful: Partisan Images, Candidate Religion, and the Electoral Impact of Party Identification," American Journal of Political Science, John Wiley & Sons, vol. 55(1), pages 42-58, January.
    4. Puig, Xavier & Ginebra, Josep, 2014. "A cluster analysis of vote transitions," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 328-344.
    5. Andrew Gelman & David K. Park & Stephen Ansolabehere & Phillip N. Price & Lorraine C. Minnite, 2001. "Models, assumptions and model checking in ecological regressions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 101-118.
    6. Jon Wakefield, 2004. "Ecological inference for 2 × 2 tables," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(3), pages 385-425, July.
    7. Ori Rosen & Wenxin Jiang & Gary King & Martin A. Tanner, 2001. "Bayesian and Frequentist Inference for Ecological Inference: The R×C Case," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 55(2), pages 134-156, July.
    8. D. James Greiner & Kevin M. Quinn, 2009. "R×C ecological inference: bounds, correlations, flexibility and transparency of assumptions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 67-81, January.
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    1. Carolina Plescia & Lorenzo De Sio, 2018. "An evaluation of the performance and suitability of R × C methods for ecological inference with known true values," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(2), pages 669-683, March.

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