Bayesian estimation of the random coefficients logit from aggregate count data

German Zenetti () and Thomas Otter

Quantitative Marketing and Economics (QME), 2014, vol. 12, issue 1, 43-84

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

Keywords: Random coefficient multinomial logit; Store-level aggregate data; Bayesian estimation; C11; M3 (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11129-013-9140-4

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