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Multiplicity of equilibria and information structures in empirical games: challenges and prospects

Author

Listed:
  • Ron Borkovsky
  • Paul Ellickson
  • Brett Gordon
  • Victor Aguirregabiria
  • Pedro Gardete
  • Paul Grieco
  • Todd Gureckis
  • Teck-Hua Ho
  • Laurent Mathevet
  • Andrew Sweeting

Abstract

Empirical models of strategic games are central to much analysis in marketing and economics. However, two challenges in applying these models to real-world data are that such models often admit multiple equilibria and that they require strong informational assumptions. The first implies that the model does not make unique predictions about the data, and the second implies that results may be driven by strong a priori assumptions about the informational setup. This article summarizes recent work that seeks to address both issues and suggests some avenues for future research. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Ron Borkovsky & Paul Ellickson & Brett Gordon & Victor Aguirregabiria & Pedro Gardete & Paul Grieco & Todd Gureckis & Teck-Hua Ho & Laurent Mathevet & Andrew Sweeting, 2015. "Multiplicity of equilibria and information structures in empirical games: challenges and prospects," Marketing Letters, Springer, vol. 26(2), pages 115-125, June.
    • Handle: RePEc:kap:mktlet:v:26:y:2015:i:2:p:115-125
    DOI: 10.1007/s11002-014-9308-z
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    Cited by:

    1. Tobias Salz & Emanuel Vespa, 2020. "Estimating dynamic games of oligopolistic competition: an experimental investigation," RAND Journal of Economics, RAND Corporation, vol. 51(2), pages 447-469, June.
    2. Victor Aguirregabiria, 2021. "Identification of firms’ beliefs in structural models of market competition," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 54(1), pages 5-33, February.
    3. Pavel Kireyev, 2016. "Markets for Ideas: Prize Structure, Entry Limits, and the Design of Ideation Contests," Harvard Business School Working Papers 16-129, Harvard Business School.
    4. Victor Aguirregabiria & Jihye Jeon, 2020. "Firms’ Beliefs and Learning: Models, Identification, and Empirical Evidence," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 56(2), pages 203-235, March.
    5. Yang, Chao & Lee, Lung-fei & Qu, Xi, 2018. "Tobit models with social interactions: Complete vs incomplete information," Regional Science and Urban Economics, Elsevier, vol. 73(C), pages 30-50.
    6. Sanchez Villalba, Miguel, 2015. "Global inspection games," Journal of Public Economics, Elsevier, vol. 128(C), pages 59-72.
    7. José‐Antonio Espín‐Sánchez & Álvaro Parra & Yuzhou Wang, 2023. "Equilibrium uniqueness in entry games with private information," RAND Journal of Economics, RAND Corporation, vol. 54(3), pages 512-540, September.
    8. Vélez-Velásquez, Juan Sebastián, 2019. "Merger effects with product complementarity: Evidence from Colombia’s telecommunications," Information Economics and Policy, Elsevier, vol. 49(C).
    9. William Rand & Roland T. Rust & Min Kim, 2018. "Complex systems: marketing’s new frontier," AMS Review, Springer;Academy of Marketing Science, vol. 8(3), pages 111-127, December.

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    More about this item

    Keywords

    Static discrete games; Dynamic games; Structural estimation; Multiplicity of equilibria; Information structures; Learning;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C57 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Econometrics of Games and Auctions
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games

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