Bayesian Estimation of the Random Coefficients Logit from Aggregate Count Data

49 Pages Posted: 9 May 2014

See all articles by German Zenetti

German Zenetti

Humboldt University of Berlin

Thomas Otter

Goethe University Frankfurt - Department of Marketing

Date Written: May 7, 2014

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 & Gupta (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 & Gupta, 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.

Keywords: Random coefficient multinomial logit, store-level aggregate data, Bayesian estimation

JEL Classification: C11, M3

Suggested Citation

Zenetti, German and Otter, Thomas, Bayesian Estimation of the Random Coefficients Logit from Aggregate Count Data (May 7, 2014). Quantitative Marketing and Economics, Vol. 12, No. 1, 2014, Available at SSRN: https://ssrn.com/abstract=2434158

German Zenetti (Contact Author)

Humboldt University of Berlin ( email )

Unter den Linden 6
Berlin, AK Berlin 10099
Germany

Thomas Otter

Goethe University Frankfurt - Department of Marketing ( email )

Frankfurt
Germany
++49.69.798.34646 (Phone)

HOME PAGE: http://www.marketing.uni-frankfurt.de/index.php?id=97?&L=1

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