This paper derives the limit experiment for nonlinear GMMmodels with weak and partial identification. We propose a theoretically-motivated class of default priors on a non-parametric nuisance parameter. These priors imply computationally tractable Bayes decision rules in the limit problem, while leaving the prior on the structural parameter free to be selected by the researcher . We further obtain quasi-Bayes decision rules as the limit of sequences in this class, and derive weighted average power-optimal identification-robust frequentist tests . Finally, we prove a Bernstein-von Mises-type result for the quasi-Bayes posterior under weak and partial identification.
This paper studies optimal decision rules, including estimators and tests, for weakly identified GMM models. We derive the limit experiment for weakly identified GMM, and propose a theoretically-motivated class of priors which give rise to quasi-Bayes decision rules as a limiting case. Together with results in the previous literature, this establishes desirable properties for the quasi-Bayes approach regardless of model identification status . We further propose weighted average power-optimal identification-robust frequentist tests and confidence sets, and prove a Bernstein-von Mises-type result for the quasi-Bayes posterior under weak identification.
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studies optimal decision rules, including estimators and tests, for weakly identified GMM models. We derive
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nonlinear GMMmodels with weak and partial identification. We
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default priors on a non-parametric nuisance parameter. These priors imply computationally tractable Bayes decision rules in the limit problem, while leaving the prior on the structural parameter free to be selected by the researcher
priors which give rise to quasi-Bayes decision rules as a limiting case. Together with results in the previous literature, this establishes desirable properties for the quasi-Bayes approach regardless of model identification status
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obtain quasi-Bayes decision rules as the limit of sequences in this class, and derive
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