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A Research Assistant's Guide to Random Coefficients Discrete Choice Models of Demand

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  • Aviv Nevo

Abstract

The study of differentiated-products markets is a central part of empirical industrial organization. Questions regarding market power, mergers, innovation, and valuation of new brands are addressed using cutting-edge econometric methods and relying on economic theory. Unfortunately, difficulty of use and computational costs have limited the scope of application of recent developments in one of the main methods for estimating demand for differentiated products: random coefficients discrete choice models. As our understanding of these models of demand has increased, both the difficulty and costs have been greatly reduced. This paper carefully discusses the latest innovations in these methods with the hope of (1) increasing the understanding, and therefore the trust, among researchers who never used these methods, and (2) reducing the difficulty of use, and therefore aiding in realizing the full potential of these methods.

Suggested Citation

  • Aviv Nevo, 1998. "A Research Assistant's Guide to Random Coefficients Discrete Choice Models of Demand," NBER Technical Working Papers 0221, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberte:0221
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    Cited by:

    1. Nevo, Aviv, 2001. "Measuring Market Power in the Ready-to-Eat Cereal Industry," Econometrica, Econometric Society, vol. 69(2), pages 307-342, March.
    2. Nevo, Aviv, 1997. "Mergers with Differentiated Products: The Case of Ready-to-Eat Cereal," Competition Policy Center, Working Paper Series qt1d53t6ts, Competition Policy Center, Institute for Business and Economic Research, UC Berkeley.
    3. Mojduszka, Eliza M., 2001. "Integration Of A Product Choice Model And A Latent Variable Model Of Nutrition Information," 2001 Annual meeting, August 5-8, Chicago, IL 20678, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    4. Pál, László & Sándor, Zsolt, 2023. "Comparing procedures for estimating random coefficient logit demand models with a special focus on obtaining global optima," International Journal of Industrial Organization, Elsevier, vol. 88(C).
    5. Charles Romeo, 2007. "A Gibbs sampler for mixed logit analysis of differentiated product markets using aggregate data," Computational Economics, Springer;Society for Computational Economics, vol. 29(1), pages 33-68, February.
    6. Khan, Beethika S., 2004. "Consumer Adoption of Online Banking: Does Distance Matter?," Department of Economics, Working Paper Series qt2bt1d76s, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    7. Beethika Khan, 2004. "Consumer Adoption of Online Banking: Does Distance Matter?," Development and Comp Systems 0407002, University Library of Munich, Germany.

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