Arbeitspapier
Monte Carlo confidence sets for identified sets
In complicated/nonlinear parametric models, it is generally hard to know whether the model parameters are point identified. We provide computationally attractive procedures to construct confidence sets (CSs) for identified sets of full parameters and of subvectors in models defined through a likelihood or a vector of moment equalities or inequalities. These CSs are based on level sets of optimal sample criterion functions (such as likelihood or optimally-weighted or continuously-updated GMM criterions). The level sets are constructed using cutoffs that are computed via Monte Carlo (MC) simulations directly from the quasi-posterior distributions of the criterions. We establish new Bernstein-von Mises (or Bayesian Wilks) type theorems for the quasi-posterior distributions of the quasi-likelihood ratio (QLR) and profile QLR in partially-identified regular models and some non-regular models. These results imply that our MC CSs have exact asymptotic frequentist coverage for identified sets of full parameters and of subvectors in partially-identified regular models, and have valid but potentially conservative coverage in models with reduced-form parameters on the boundary. Our MC CSs for identified sets of subvectors are shown to have exact asymptotic coverage in models with singularities. We also provide results on uniform validity of our CSs over classes of DGPs that include point and partially identified models. We demonstrate good finite-sample coverage properties of our procedures in two simulation experiments. Finally, our procedures are applied to two non-trivial empirical examples: an airline entry game and a model of trade flows.
- Language
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Englisch
- Bibliographic citation
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Series: cemmap working paper ; No. CWP43/17
- Classification
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Wirtschaft
- Event
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Geistige Schöpfung
- (who)
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Chen, Xiaohong
Christensen, Timothy M.
Tamer, Elie
- Event
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Veröffentlichung
- (who)
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Centre for Microdata Methods and Practice (cemmap)
- (where)
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London
- (when)
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2017
- DOI
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doi:10.1920/wp.cem.2017.4317
- Handle
- Last update
-
10.03.2025, 11:45 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Chen, Xiaohong
- Christensen, Timothy M.
- Tamer, Elie
- Centre for Microdata Methods and Practice (cemmap)
Time of origin
- 2017
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Semiparametric efficiency bound for models of sequential moment restrictions containing unknown functions
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Penalized sieve estimation and inference of semi-nonparametric dynamic models: A selective review
Optimal sup-norm rates and uniform inference on nonlinear functionals of nonparametric IV regression
Efficient estimation of semiparametric conditional moment models with possibly nonsmooth residuals
Optimal sup-norm rates, adaptivity and inference in nonparametric instrumental variables estimation
Optimal uniform convergence rates for sieve nonparametric instrumental variables regression
Optimal uniform convergence rates and asymptotic normality for series estimators under weak dependence and weak conditions
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Semiparametric efficiency bound for models of sequential moment restrictions containing unknown functions
Optimal sup-norm rates and uniform inference on nonlinear functionals of nonparametric IV regression
Estimation of nonparametric conditional moment models with possibly nonsmooth moments