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Dynamic Games in Empirical Industrial Organization

Victor Aguirregabiria, Allan Collard-Wexler, Stephen P. Ryan

arXiv 3 Sep 2021 · Econometrics · 16 citations (OpenAlex)

arXiv:2109.01725 · PDF · DOI · OpenAlex · Extracted main text

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.

Citation extraction

269
references
613
in-text mentions
269
distinct cited
21
self-citations
57,975
main-text words

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Aguirregabiria, Victor, Mira, Pedro (2007) Sequential estimation of dynamic discrete games self1.000175100%
2Bajari, Patrick, Benkard, C. Lanier, Levin, Jonathan (2007) Estimating dynamic models of imperfect competition1.000164100%
3Pakes, Ariel, McGuire, Paul (1994) Computing Markov-perfect nash equilibria: numerical implications of a dynamic differentiated product model1.000154100%
4Ericson, Richard, Pakes, Ariel (1995) Markov-perfect industry dynamics: a framework for empirical work1.000133100%
5Bresnahan, Timothy F., Reiss, Peter C (1991) Entry and competition in concentrated markets1.000104100%
6Igami, Mitsuru (2017) Estimating the innovator's dilemma: structural analysis of creative destruction in the hard disk drive industry, 1981–19981.00093100%
7Pakes, Ariel, Ostrovsky, Michael, Berry, Steven (2007) Simple estimators for the parameters of discrete dynamic games (with entry/exit examples)1.00084100%
8Benkard, C. Lanier (2004) A dynamic analysis of the market for wide-bodied commercial aircraft1.00073100%
9Goettler, Ronald L., Gordon, Brett R (2011) Does AMD spur Intel to innovate more?1.00073100%
10Pesendorfer, Martin, Schmidt-Dengler, Philipp (2008) Asymptotic least squares estimators for dynamic games1.00073100%

Showing the top 10 of 269 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Identification and Estimation of Dynamic Games with Unknown Information Structure0.73732
2Leveraging Uniformization and Sparsity for Estimation and Computation of Continuous-Time Dynamic Discrete Choice Games0.40511
3Reinforcement Learning Based Computationally Efficient Conditional Choice Simulation Estimation of Dynamic Discrete Choice Models0.40511
4Identification and Estimation of Demand Models with Endogenous Product Entry and Exit0.00021