Partial Identification and Inference for Dynamic Models and Counterfactuals

Kalouptsidi, Myrto; Kitamura, Yuichi; Lima, Lucas Chaves; Souza-Rodrigues, Eduardo

doi:10.2139/ssrn.3535147

Cited by 5 publications

(5 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Aguirregabiria and Suzuki 2014 describe this approach in the context of the model of market entry/exit. Kalouptsidi et al 2020 present a general partial identification approach for the implementation of counterfactuals in dynamic discrete choice models.…”

Section: Relaxing Restrictions (Id1) To (Id8)mentioning

confidence: 99%

Dynamic Games in Empirical Industrial Organization

Aguirregabiria¹,

Collard-Wexler²,

Ryan³

2021

Preprint

View full text Add to dashboard Cite

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 * We are grateful to the editors and to four anonymous referees for constructive comments and suggestions. We would like to thank Seohee Kim for research assistance, and

show abstract

Section: Relaxing Restrictions (Id1) To (Id8)mentioning

confidence: 99%

Dynamic Games in Empirical Industrial Organization

Aguirregabiria¹,

Collard-Wexler²,

Ryan³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…At a high level, the identification component of this paper is reminiscent of Ichimura and Taber (2000), who discuss a method for performing ex-ante policy experiments in the treatment effect literature without estimating the structural parameters, and without specifying the error distribution. More recent examples of counterfactual analysis without first estimating the (identified set for the) structural parameters can be found in Syrgkanis et al (2018), Tebaldi et al (2019) and Kalouptsidi et al (2019).…”

Section: Related Literaturesmentioning

confidence: 99%

Policy Transforms and Learning Optimal Policies

Russell

2020

Preprint

View full text Add to dashboard Cite

We study the problem of choosing optimal policy rules in uncertain environments using models that may be incomplete and/or partially identified. We consider a policymaker who wishes to choose a policy to maximize a particular counterfactual quantity called a policy transform. We characterize learnability of a set of policy options by the existence of a decision rule that closely approximates the maximin optimal value of the policy transform with high probability. Sufficient conditions are provided for the existence of such a rule. However, learnability of an optimal policy is an ex-ante notion (i.e. before observing a sample), and so ex-post (i.e. after observing a sample) theoretical guarantees for certain policy rules are also provided. Our entire approach is applicable when the distribution of unobservables is not parametrically specified, although we discuss how semiparametric restrictions can be used. Finally, we show possible applications of the procedure to a simultaneous discrete choice example and a program evaluation example.

show abstract

“…counterfactuals are point identified such as those characterized by linear changes in payoffs (the so called "additive transfers counterfactuals" in Kalouptsidi, Scott, and Souza-Rodrigues (2017)). See also Kalouptsidi, Kitamura, Lima, and Souza-Rodrigues (2020) for partial identification of counterfactuals in a similar context.…”

Section: Related Literaturementioning

confidence: 99%

A Decomposition Approach to Counterfactual Analysis in Game-Theoretic Models

Canen¹,

Song²

2020

Preprint

View full text Add to dashboard Cite

Decomposition methods are often used to produce counterfactual predictions of outcomes when a policy variable changes. The methods project the observed relationship between the policy variable and the endogenous variable to a counterfactual environment. However, when the endogenous variable is generated by an agent's strategy in a game-theoretic setting and the agent has an incentive to deviate from his strategy after the policy, such predictions are hard to justify. In this paper, we use a generic model of a Bayesian game with the solution concept of Bayes Correlated Equilibria of Bergemann and Morris (2016) and show that if the information structure is rich enough and the equilibrium selection rule satisfies a weak invariance property, decomposition-based predictions are identical to equilibrium-based predictions. This result opens up the possibility for applying a decomposition method for counterfactual analysis in various strategic settings without specifying and estimating details of the game. We illustrate our message by revisiting an empirical analysis in Ciliberto and Tamer (2009) on entry decisions of firms in the airline industry.

show abstract

Partial Identification and Inference for Dynamic Models and Counterfactuals

Cited by 5 publications

References 44 publications

Dynamic Games in Empirical Industrial Organization

Dynamic Games in Empirical Industrial Organization

Policy Transforms and Learning Optimal Policies

A Decomposition Approach to Counterfactual Analysis in Game-Theoretic Models

Contact Info

Product

Resources

About