Revisiting Identification Concepts in Bayesian Analysis - HAL-SHS - Sciences de l'Homme et de la Société Accéder directement au contenu
Article Dans Une Revue Annals of Economics and Statistics Année : 2021

Revisiting Identification Concepts in Bayesian Analysis

Résumé

This paper studies the role played by identification in the Bayesian analysis of statistical and econometric models. First, for unidentified models we demonstrate that there are situations where the introduction of a non-degenerate prior distribution can make a parameter that is nonidentified in frequentist theory identified in Bayesian theory. In other situations, it is preferable to work with the unidentified model and construct a Markov Chain Monte Carlo (MCMC) algorithms for it instead of introducing identifying assumptions. Second, for partially identified models we demonstrate how to construct the prior and posterior distributions for the identified set parameter and how to conduct Bayesian analysis. Finally, for models that contain some parameters that are identified and others that are not we show that marginalizing out the identified parameter from the likelihood with respect to its conditional prior, given the nonidentified parameter, allows the data to be informative about the nonidentified and partially identified parameter. The paper provides examples and simulations that illustrate how to implement our techniques.
Fichier principal
Vignette du fichier
Florens_Simoni_Manuscript2.pdf (574.92 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03504692 , version 1 (29-12-2021)

Identifiants

Citer

Jean-Pierre Florens, Anna Simoni. Revisiting Identification Concepts in Bayesian Analysis. Annals of Economics and Statistics, 2021, 144, pp.1-38. ⟨10.15609/annaeconstat2009.144.0001⟩. ⟨hal-03504692⟩
72 Consultations
189 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More