Florens, Jean-Pierre and Simoni, Anna (2021) Gaussian Processes and Bayesian Moment Estimation. Journal of Business and Economic Statistics, vol. 39 (n° 2). pp. 482-492.

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Identification Number : 10.1080/07350015.2019.1668799

Abstract

Given a set of moment restrictions (MRs) that overidentify a parameter θ, we investigate a semiparametric Bayesian approach for inference on θ that does not restrict the data distribution F apart from the MRs. As main contribution, we construct a degenerate Gaussian process prior that, conditionally on θ, restricts the F generated by this prior to satisfy the MRs with probability one. Our prior works even in the more involved case where the number of MRs is larger than the dimension of θ. We demonstrate that the corresponding posterior for θ is computationally convenient. Moreover, we show that there exists a link between our procedure, the generalized empirical likelihood with quadratic criterion and the limited information likelihood-based procedures. We provide a frequentist validation of our procedure by showing consistency and asymptotic normality of the posterior distribution of θ. The finite sample properties of our method are illustrated through Monte Carlo experiments and we provide an application to demand estimation in the airline market.

Item Type: Article
Language: English
Date: 2021
Refereed: Yes
Subjects: B- ECONOMIE ET FINANCE
Divisions: TSE-R (Toulouse)
Site: UT1
Date Deposited: 22 Mar 2021 09:02
Last Modified: 10 Sep 2021 11:32
OAI Identifier: oai:tse-fr.eu:123945
URI: https://publications.ut-capitole.fr/id/eprint/33839
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