Bayes, Bootstrap, and Moments

Jean-Pierre Florens () and Jean-Marie Rolin
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Jean-Pierre Florens: Université Toulouse Capitole, Toulouse School of Economics
Jean-Marie Rolin: Université Catholique de Louvain, Institut de Statistique

Chapter Chapter 5 in Nonparametric Bayesian Inference, 2024, pp 91-128 from Springer

Abstract: Abstract This chapter presents a Bayesian analysis of semiparametric models in which the vector of parameters of interest is characterized by a moment equation. Powerful representations of the Dirichlet processes are used and provide an efficient numerical strategy to deal with such models. The so-called noninformative prior specification gives a Bayesian interpretation of the bootstrap method, but some pathologies of this prior measure are pointed out. Several numerical applications illustrate the presentation.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-61329-6_5

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DOI: 10.1007/978-3-031-61329-6_5

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