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Bayesian estimation of the random coefficients logit from aggregate count data

Author

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  • German Zenetti
  • Thomas Otter

Abstract

The random coefficients logit model is a workhorse in marketing and empirical industrial organizations research. When only aggregate data are available, it is customary to calibrate the model based on market shares as data input, even if the data are available in the form of aggregate counts. However, market shares are functionally related to model primitives in the random coefficients model whereas finite aggregate counts are only probabilistic functions of these model primitives. A recent paper by Park and Gupta (Journal of Marketing Research, 46(4), 531–543 2009 ) stresses this distinction but is hamstrung by numerical problems when demonstrating its potential practical importance. We develop Bayesian inference for the likelihood function proposed by Park and Gupta (Journal of Marketing Research, 46(4), 531–543 2009 ), sidestepping the numerical problem encountered by these authors. We show how taking account of the amount of information about shares by modeling counts directly results in improved inference. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • German Zenetti & Thomas Otter, 2014. "Bayesian estimation of the random coefficients logit from aggregate count data," Quantitative Marketing and Economics (QME), Springer, vol. 12(1), pages 43-84, March.
    • Handle: RePEc:kap:qmktec:v:12:y:2014:i:1:p:43-84
    DOI: 10.1007/s11129-013-9140-4
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    Cited by:

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    More about this item

    Keywords

    Random coefficient multinomial logit; Store-level aggregate data; Bayesian estimation; C11; M3;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising

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