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Discrete Choice Models With Multiple Unobserved Choice Characteristics

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  • Susan Athey
  • Guido W. Imbens

Abstract

Since the pioneering work by Daniel McFadden, utility-maximization-based multinomial response models have become important tools of empirical researchers. Various generalizations of these models have been developed to allow for unobserved heterogeneity in taste parameters and choice characteristics. Here we investigate how rich a specification of the unobserved components is needed to rationalize arbitrary choice patterns in settings with many individual decision makers, multiple markets, and large choice sets. We find that if one restricts the utility function to be monotone in the unobserved choice characteristics, then up to two unobserved choice characteristics may be needed to rationalize the choices. Copyright 2007 by the Economics Department Of The University Of Pennsylvania And Osaka University Institute Of Social And Economic Research Association.

Suggested Citation

  • Susan Athey & Guido W. Imbens, 2007. "Discrete Choice Models With Multiple Unobserved Choice Characteristics," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 48(4), pages 1159-1192, November.
    • Handle: RePEc:ier:iecrev:v:48:y:2007:i:4:p:1159-1192
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    Cited by:

    1. Christopher T. Conlon & Julie Holland Mortimer, 2013. "Demand Estimation under Incomplete Product Availability," American Economic Journal: Microeconomics, American Economic Association, vol. 5(4), pages 1-30, November.
    2. Haoge Chang & Yusuke Narita & Kota Saito, 2022. "Approximating Choice Data by Discrete Choice Models," Papers 2205.01882, arXiv.org, revised Dec 2023.
    3. Burda, Martin & Harding, Matthew & Hausman, Jerry, 2008. "A Bayesian mixed logit-probit model for multinomial choice," Journal of Econometrics, Elsevier, vol. 147(2), pages 232-246, December.
    4. Steven Berry & Philip Haile, 2016. "Identification in Differentiated Products Markets," Annual Review of Economics, Annual Reviews, vol. 8(1), pages 27-52, October.
    5. Steven T. Berry & Philip A. Haile, 2009. "Nonparametric Identification of Multinomial Choice Demand Models with Heterogeneous Consumers," Cowles Foundation Discussion Papers 1718, Cowles Foundation for Research in Economics, Yale University, revised Mar 2010.
    6. Jun, Sung Jae & Pinkse, Joris, 2020. "Counterfactual prediction in complete information games: Point prediction under partial identification," Journal of Econometrics, Elsevier, vol. 216(2), pages 394-429.
    7. Rohlfs, Chris & Sullivan, Ryan & Kniesner, Thomas J., 2013. "Hedonic Estimation under Very General Conditions Using Experimental and Quasi-Experimental Designs," IZA Discussion Papers 7554, IZA Network @ LISER.
    8. Steven T. Berry & Philip A. Haile, 2014. "Identification in Differentiated Products Markets Using Market Level Data," Econometrica, Econometric Society, vol. 82, pages 1749-1797, September.
    9. Eric Gautier & Yuichi Kitamura, 2013. "Nonparametric Estimation in Random Coefficients Binary Choice Models," Econometrica, Econometric Society, vol. 81(2), pages 581-607, March.
    10. Eriksen, Michael D. & Kniesner, Thomas J. & Rohlfs, Chris & Sullivan, Ryan, 2016. "Toward more general hedonic estimation: Clarifying the roles of alternative experimental designs with an application to a housing attribute," Regional Science and Urban Economics, Elsevier, vol. 57(C), pages 54-62.
    11. Okwelum, Edson, 2015. "Consumer Heterogeneity and Gasoline Price Response: Implications for Optimal Tax policy," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205897, Agricultural and Applied Economics Association.
    12. Ian M. McCarthy & Rusty Tchernis, 2010. "Search costs and Medicare plan choice," Health Economics, John Wiley & Sons, Ltd., vol. 19(10), pages 1142-1165, October.
    13. Robert Donnelly & Francisco J. R. Ruiz & David Blei & Susan Athey, 2021. "Correction to: Counterfactual inference for consumer choice across many product categories," Quantitative Marketing and Economics (QME), Springer, vol. 19(3), pages 409-409, December.
    14. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2012. "Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content," Marketing Science, INFORMS, vol. 31(3), pages 493-520, May.

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