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Estimation of Random-Coefficient Demand Models: Two Empiricists' Perspective

Author

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  • Christopher R. Knittel

    (Sloan School of Management, MIT, and NBER)

  • Konstantinos Metaxoglou

    (Bates White LLC)

Abstract

We document the numerical challenges we experienced estimating random-coefficient demand models as in Berry, Levinsohn, and Pakes (1995) using two well-known data sets and a thorough optimization design. The optimization algorithms often converge at points where the first- and second-order optimality conditions fail. There are also cases of convergence at local optima. On convergence, the variation in the values of the parameter estimates translates into variation in the models' economic predictions. Price elasticities and changes in consumer and producer welfare following hypothetical merger exercises vary at least by a factor of 2 and up to a factor of 5. © 2014 The President and Fellows of Harvard College and the Massachusetts Institute of Technology

Suggested Citation

  • Christopher R. Knittel & Konstantinos Metaxoglou, 2014. "Estimation of Random-Coefficient Demand Models: Two Empiricists' Perspective," The Review of Economics and Statistics, MIT Press, vol. 96(1), pages 34-59, March.
    • Handle: RePEc:tpr:restat:v:96:y:2014:i:1:p:34-59
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    Keywords

    random-coefficient demand; optimization;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • L44 - Industrial Organization - - Antitrust Issues and Policies - - - Antitrust Policy and Public Enterprise, Nonprofit Institutions, and Professional Organizations

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