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Dynamic Games in Empirical Industrial Organization

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  • Aguirregabiria, Victor
  • Collard-Wexler, Allan
  • Ryan, Stephen

Abstract

This survey is organized around three main topics: models, econometrics, and empirical applications. Section 2 presents the theoretical framework, introduces the concept of Markov Perfect Nash Equilibrium, discusses existence and multiplicity, and describes the representation of this equilibrium in terms of conditional choice probabilities. We also discuss extensions of the basic framework, including models in continuous time, the concepts of oblivious equilibrium and experience-based equilibrium, and dynamic games where firms have non-equilibrium beliefs. In section 3, we first provide an overview of the types of data used in this literature, before turning to a discussion of identification issues and results, and estimation methods. We review different methods to deal with multiple equilibria and large state spaces. We also describe recent developments for estimating games in continuous time and incorporating serially correlated unobservables, and discuss the use of machine learning methods to solving and estimating dynamic games. Section 4 discusses empirical applications of dynamic games in IO. We start describing the first empirical applications in this literature during the early 2000s. Then, we review recent applications dealing with innovation, antitrust and mergers, dynamic pricing, regulation, product repositioning, advertising, uncertainty and investment, airline network competition, dynamic matching, and natural resources. We conclude with our view of the progress made in this literature and the remaining challenges.

Suggested Citation

  • Aguirregabiria, Victor & Collard-Wexler, Allan & Ryan, Stephen, 2021. "Dynamic Games in Empirical Industrial Organization," CEPR Discussion Papers 16514, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:16514
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    2. Paul S. Koh, 2022. "Estimating Dynamic Games with Unknown Information Structure," Papers 2205.03706, arXiv.org, revised May 2022.
    3. Takeshi Fukasawa, 2022. "Firm's Static Behavior under Dynamic Demand," Discussion Paper Series DP2022-19, Research Institute for Economics & Business Administration, Kobe University, revised Sep 2022.

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    3. Aguirregabiria, Victor & Mira, Pedro, 2010. "Dynamic discrete choice structural models: A survey," Journal of Econometrics, Elsevier, vol. 156(1), pages 38-67, May.
    4. Kalouptsidi, Myrto & Scott, Paul T. & Souza-Rodrigues, Eduardo, 2021. "Linear IV regression estimators for structural dynamic discrete choice models," Journal of Econometrics, Elsevier, vol. 222(1), pages 778-804.
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    More about this item

    Keywords

    Counterfactuals; Dynamic games; estimation; identification; industrial organization; market competition; Structural models;
    All these keywords.

    JEL classification:

    • C57 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Econometrics of Games and Auctions
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets

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