Arbeitspapier

Uncertain identification

Uncertainty about the choice of identifying assumptions is common in causal studies, but is often ignored in empirical practice. This paper considers uncertainty over models that impose different identifying assumptions, which, in general, leads to a mix of point- and set-identified models. We propose performing inference in the presence of such uncertainty by generalizing Bayesian model averaging. The method considers multiple posteriors for the set-identified models and combines them with a single posterior for models that are either point-identified or that impose non-dogmatic assumptions. The output is a set of posteriors (post-averaging ambiguous belief ) that are mixtures of the single posterior and any element of the class of multiple posteriors, with weights equal to the posterior model probabilities. We suggest reporting the set of posterior means and the associated credible region in practice, and provide a simple algorithm to compute them. We establish that the prior model probabilities are updated when the models are "distinguishable" and/or they specify different priors for reduced-form parameters, and characterize the asymptotic behavior of the posterior model probabilities. The method provides a formal framework for conducting sensitivity analysis of empirical findings to the choice of identifying assumptions. In a standard monetary model, for example, we show that, in order to support a negative response of output to a contractionary monetary policy shock, one would need to attach a prior probability greater than 0.05 to the validity of the assumption that prices do not react contemporaneously to the shock.

Sprache
Englisch

Erschienen in
Series: cemmap working paper ; No. CWP33/20

Klassifikation
Wirtschaft
Thema
Partial Identification
Sensitivity Analysis
Model Averaging
Bayesian Robustness
Ambiguity

Ereignis
Geistige Schöpfung
(wer)
Giacomini, Raffaella
Kitagawa, Toru
Volpicella, Alessio
Ereignis
Veröffentlichung
(wer)
Centre for Microdata Methods and Practice (cemmap)
(wo)
London
(wann)
2020

DOI
doi:10.1920/wp.cem.2020.3320
Handle
Letzte Aktualisierung
10.03.2025, 11:44 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Giacomini, Raffaella
  • Kitagawa, Toru
  • Volpicella, Alessio
  • Centre for Microdata Methods and Practice (cemmap)

Entstanden

  • 2020

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