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

Author

Listed:
  • 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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    Cited by:

    1. Kaiser, Ulrich & Mendez, Susan J. & Rønde, Thomas & Ullrich, Hannes, 2014. "Regulation of pharmaceutical prices: Evidence from a reference price reform in Denmark," Journal of Health Economics, Elsevier, vol. 36(C), pages 174-187.
    2. Fosgerau, Mogens & de Palma, André, 2015. "Demand systems for market shares," MPRA Paper 62106, University Library of Munich, Germany.
    3. Friberg, Richard & Romahn, André, 2015. "Divestiture requirements as a tool for competition policy: A case from the Swedish beer market," International Journal of Industrial Organization, Elsevier, vol. 42(C), pages 1-18.
    4. Mogens Fosgerau & André De Palma, 2016. "Generalized entropy models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-01291347, HAL.
    5. Blonigen, Bruce A. & Knittel, Christopher R. & Soderbery, Anson, 2017. "Keeping it fresh: Strategic product redesigns and welfare," International Journal of Industrial Organization, Elsevier, vol. 53(C), pages 170-214.
    6. repec:eee:transa:v:103:y:2017:i:c:p:198-210 is not listed on IDEAS
    7. Hyungsik Roger Moon & Matthew Shum & Martin Weidner, 2012. "Estimation of random coefficients logit demand models with interactive fixed effects," CeMMAP working papers CWP08/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Mattia Girotti & Richard Meade, 2017. "U.S. Savings Banks' Demutualization and Depositor Welfare," Working Papers 2017-08 JEL Classificatio, Auckland University of Technology, Department of Economics.
    9. Jose A. Guajardo & Morris A. Cohen & Serguei Netessine, 2016. "Service Competition and Product Quality in the U.S. Automobile Industry," Management Science, INFORMS, vol. 62(7), pages 1860-1877, July.
    10. repec:eee:indorg:v:53:y:2017:i:c:p:267-305 is not listed on IDEAS
    11. Jiekai ZHANG, 2016. "The impact of advertising length caps on TV: Evidence from the French broadcast TV industry," Working Papers 16-06, NET Institute.
    12. repec:bla:jorssc:v:66:y:2017:i:5:p:997-1013 is not listed on IDEAS
    13. Mogens, Fosgerau, 2016. "A regression model of product differentiation," MPRA Paper 72786, University Library of Munich, Germany.
    14. Isaiah Andrews & Matthew Gentzkow & Jesse M. Shapiro, 2017. "Measuring the Sensitivity of Parameter Estimates to Estimation Moments," The Quarterly Journal of Economics, Oxford University Press, vol. 132(4), pages 1553-1592.
    15. Escobari, Diego, 2017. "Airport, airline and departure time choice and substitution patterns: An empirical analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 198-210.
    16. Pauline Givord & Céline Grislain-Letrémy & Helene Naegele, 2014. "How Does Fuel Taxation Impact New Car Purchases?: An Evaluation Using French Consumer-Level Data," Discussion Papers of DIW Berlin 1428, DIW Berlin, German Institute for Economic Research.
    17. Arne Risa Hole & Hong Il Yoo, 2017. "The use of heuristic optimization algorithms to facilitate maximum simulated likelihood estimation of random parameter logit models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(5), pages 997-1013, November.
    18. Mogens Fosgerau & André De Palma, 2016. "Generalized entropy models," Working Papers hal-01291347, HAL.
    19. Vincenzo Mollisi & Gabriele Rovigatti, 2017. "Theory and Practice of TFP Estimation: the Control Function Approach Using Stata," CEIS Research Paper 399, Tor Vergata University, CEIS, revised 14 Feb 2017.

    More about this item

    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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