Estimation under ambiguity

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: cemmap working paper ; No. CWP24/19

Klassifikation und Themen

• Klassifikation: Wirtschaft

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Giacomini, Raffaella ; Kitagawa, Toru ; Uhlig, Harald
• Ereignis: Veröffentlichung
• (wer): Centre for Microdata Methods and Practice (cemmap)
• (wo): London
• (wann): 2019

Weitere Informationen

• DOI: doi:10.1920/wp.cem.2019.2419
• Handle: http://hdl.handle.net/10419/211117
• Letzte Aktualisierung: 10.03.2025, 11:47 MEZ