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Estimation under Ambiguity

Giacomini, R; Kitagawa, T; Uhlig, H; (2019) Estimation under Ambiguity. (Cemmap Working Paper 24/19). Institute for Fiscal Studies: London, UK.

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To perform Bayesian analysis of a partially identified structural model, two distinct approaches exist: standard Bayesian inference, which assumes a single prior for the structural parameters, including the non-identified ones; and multiple-prior Bayesian inference, which assumes full ambiguity for the non-identified parameters. The prior inputs considered by these two extreme approaches can often be a poor representation of the researcher’s prior knowledge in practice. This paper fills the large gap between the two approaches by proposing a multiple-prior Bayesian analysis that can simultaneously incorporate a probabilistic belief for the non-identified parameters and a concern about misspecification of this belief. Our proposal introduces a benchmark prior representing the researcher’s partially credible probabilistic belief for non-identified parameters, and a set of priors formed in its Kullback-Leibler (KL) neighborhood, whose radius controls the “degree of ambiguity.” We obtain point estimators and optimal decisions involving non-identified parameters by solving a conditional gamma-minimax problem, which we show is analytically tractable and easy to solve numerically. We derive the remarkably simple analytical properties of the proposed procedure in the limiting situations where the radius of the KL neighborhood and/or the sample size are large. Our procedure can also be used to perform global sensitivity analysis.

Type: Working / discussion paper
Title: Estimation under Ambiguity
DOI: 10.1920/wp.cem.2019.2419
Publisher version: http://doi.org/10.1920/wp.cem.2019.2419
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Economics
URI: https://discovery.ucl.ac.uk/id/eprint/10087226
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