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Bayesian analysis of random coefficient logit models using aggregate data

Author

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  • Jiang, Renna
  • Manchanda, Puneet
  • Rossi, Peter E.

Abstract

We present a Bayesian approach for analyzing aggregate level sales data in a market with differentiated products. We consider the aggregate share model proposed by Berry et al. [Berry, Steven, Levinsohn, James, Pakes, Ariel, 1995. Automobile prices in market equilibrium. Econometrica. 63 (4), 841-890], which introduces a common demand shock into an aggregated random coefficient logit model. A full likelihood approach is possible with a specification of the distribution of the common demand shock. We introduce a reparameterization of the covariance matrix to improve the performance of the random walk Metropolis for covariance parameters. We illustrate the usefulness of our approach with both actual and simulated data. Sampling experiments show that our approach performs well relative to the GMM estimator even in the presence of a mis-specified shock distribution. We view our approach as useful for those who are willing to trade off one additional distributional assumption for increased efficiency in estimation.

Suggested Citation

  • Jiang, Renna & Manchanda, Puneet & Rossi, Peter E., 2009. "Bayesian analysis of random coefficient logit models using aggregate data," Journal of Econometrics, Elsevier, vol. 149(2), pages 136-148, April.
  • Handle: RePEc:eee:econom:v:149:y:2009:i:2:p:136-148
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    References listed on IDEAS

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    Cited by:

    1. Reynaert, Mathias & Verboven, Frank, 2014. "Improving the performance of random coefficients demand models: The role of optimal instruments," Journal of Econometrics, Elsevier, vol. 179(1), pages 83-98.
    2. 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.
    3. Benjamin Reed Shiller, 2013. "Digital distribution and the prohibition of resale markets for information goods," Quantitative Marketing and Economics (QME), Springer, vol. 11(4), pages 403-435, December.
    4. Abidoye, Babatunde Oluwakayode, 2010. "Bayesian inference in modeling recreation demand," ISU General Staff Papers 201001010800002496, Iowa State University, Department of Economics.
    5. Babatunde O. Abidoye & Joseph A. Herriges & Justin L. Tobias, 2012. "Controlling for Observed and Unobserved Site Characteristics in RUM Models of Recreation Demand," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 94(5), pages 1070-1093.
    6. repec:eee:jouret:v:92:y:2016:i:3:p:352-372 is not listed on IDEAS
    7. repec:kap:qmktec:v:15:y:2017:i:1:d:10.1007_s11129-017-9180-2 is not listed on IDEAS
    8. Zhou, Yiyi, 2012. "Failure to Launch in Two-Sided Markets: A Study of the U.S. Video Game Market," MPRA Paper 42002, University Library of Munich, Germany.
    9. Klomp, Jeroen, 2010. "Causes of banking crises revisited," The North American Journal of Economics and Finance, Elsevier, vol. 21(1), pages 72-87, March.
    10. Andrés Musalem & Marcelo Olivares & Eric T. Bradlow & Christian Terwiesch & Daniel Corsten, 2010. "Structural Estimation of the Effect of Out-of-Stocks," Management Science, INFORMS, vol. 56(7), pages 1180-1197, July.
    11. Sugawara, Shinya, 2012. "A nonparametric Bayesian approach for counterfactual prediction with an application to the Japanese private nursing home market," MPRA Paper 42154, University Library of Munich, Germany.
    12. Zenetti, German, 2010. "A Note on 'Bayesian analysis of the random coefficient model using aggregate data', an alternative approach," MPRA Paper 26449, University Library of Munich, Germany.
    13. Fernández-Antolín, Anna & Guevara, C. Angelo & de Lapparent, Matthieu & Bierlaire, Michel, 2016. "Correcting for endogeneity due to omitted attitudes: Empirical assessment of a modified MIS method using RP mode choice data," Journal of choice modelling, Elsevier, vol. 20(C), pages 1-15.
    14. Bart Bronnenberg & Jean Dubé & Carl Mela & Paulo Albuquerque & Tulin Erdem & Brett Gordon & Dominique Hanssens & Guenter Hitsch & Han Hong & Baohong Sun, 2008. "Measuring long-run marketing effects and their implications for long-run marketing decisions," Marketing Letters, Springer, vol. 19(3), pages 367-382, December.

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