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Removing Heterogeneity Bias from Logit Model Estimation

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Author Info

  • J. Morgan Jones

    (University of North Carolina)

  • Jane T. Landwehr

    (New York University)

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    Abstract

    This paper introduces a new estimation procedure for the logit model that is an extension of a conditional estimation procedure developed by Chamberlain (Chamberlain, G. 1978. On the use of panel data. Working paper, Harvard University, 1–52; Chamberlain, G. 1980. Analysis of covariance with qualitative data. 225–238.). The method eliminates heterogeneity bias which is present in the estimates produced by traditional estimation techniques. It improves the goodness of fit and explanatory power of these models, while incorporating purchase event feedback.

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    File URL: http://dx.doi.org/10.1287/mksc.7.1.41
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    Bibliographic Info

    Article provided by INFORMS in its journal Marketing Science.

    Volume (Year): 7 (1988)
    Issue (Month): 1 ()
    Pages: 41-59

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    Handle: RePEc:inm:ormksc:v:7:y:1988:i:1:p:41-59

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    Related research

    Keywords: logit; heterogeneous; purchase-event feedback; brand choice;

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    Cited by:
    1. José M. Labeaga & Mercedes Martos-Partal, 2007. "A Proposal to Distinguish State Dependence and Unobserved Heterogeneity in Binary Brand Choice Models," Working Papers 2007-02, FEDEA.
    2. Chintagunta, Pradeep & Kyriazidou, Ekaterini & Perktold, Josef, 2001. "Panel data analysis of household brand choices," Journal of Econometrics, Elsevier, vol. 103(1-2), pages 111-153, July.
    3. Ulimwengu, John & Sanyal, Prabuddha, 2011. "Joint estimation of farmers' stated willingness to pay for agricultural services:," IFPRI discussion papers 1070, International Food Policy Research Institute (IFPRI).
    4. Hilger, James & Hanemann, W. Michael, 2008. "The Impact of Water Quality on Southern California Beach Recreation: A Finite Mixture Model Approach," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt9v17r715, Department of Agricultural & Resource Economics, UC Berkeley.
    5. Baltas, George & Doyle, Peter, 2001. "Random utility models in marketing research: a survey," Journal of Business Research, Elsevier, vol. 51(2), pages 115-125, February.
    6. Koster, Hans R.A. & van Ommeren, Jos & Rietveld, Piet, 2014. "Estimation of semiparametric sorting models: Explaining geographical concentration of business services," Regional Science and Urban Economics, Elsevier, vol. 44(C), pages 14-28.
    7. Wicker, Pamela & Prinz, Joachim & von Hanau, Tassilo, 2012. "Estimating the value of national sporting success," Sport Management Review, Elsevier, vol. 15(2), pages 200-210.
    8. Abramson, Charles & Buchmueller, Thomas & Currim, Imran, 1998. "Models of health plan choice," European Journal of Operational Research, Elsevier, vol. 111(2), pages 228-247, December.
    9. Roy, Abhik, 1998. "An error components approach to segmentation and modelling brand choice dynamics," Journal of Economic Psychology, Elsevier, vol. 19(4), pages 463-484, August.
    10. González-Benito, Óscar, 2004. "Random effects choice models: seeking latent predisposition segments in the context of retail store format selection," Omega, Elsevier, vol. 32(2), pages 167-177, April.

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