Counterfactual analysis under partial identification using locally robust refinement

Bibliographic Data

ID21653258
AuthorsNathan Canen (0000-0002-6444-145X, Department of Economics University of Houston Houston Texas USA), Kyungchul Song (0000-0002-8497-5934, Vancouver School of Economics University of British Columbia Vancouver British Columbia Canada, corresponding author)
Year2021
Volume36
Issue4
Pages416-436
Publication date2021-06-01
Open AccessYes
TypeARTICLE
VenueJournal of Applied Econometrics (JOURNAL)
Journal identifiersISSN: 1099-1255 • E-ISSN: 0883-7252
PublisherWiley (PUBLISHER • GB)
DOI10.1002/jae.2819
OpenAlexW3157535463
LanguageEN
References cited38

Structural models that admit multiple reduced forms, such as game‐theoretic models with multiple equilibria, pose challenges in practice, especially when parameters are set identified and the identified set is large. In such cases, researchers often choose to focus on a particular subset of equilibria for counterfactual analysis, but this choice can be hard to justify. This paper shows that some parameter values can be more “desirable” than others for counterfactual analysis, even if they are empirically equivalent given the data. In particular, within the identified set, some counterfactual predictions can exhibit more robustness than others against local perturbations of the reduced forms (e.g., the equilibrium selection rule). We provide a representation of this subset, which can be used to simplify the implementation. We illustrate our message using moment inequality models and provide an empirical application based on a model with top coded data

Counterfactual thinking · Econometrics · Mathematical economics · Mathematical optimization · Climate Change Policy and Economics · Computer Science · Decision-Making and Behavioral Economics · Economic and Environmental Valuation · Mathematics

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