Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/253597 
Erscheinungsjahr: 
2021
Quellenangabe: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 12 [Issue:] 2 [Publisher:] The Econometric Society [Place:] New Haven, CT [Year:] 2021 [Pages:] 313-350
Verlag: 
The Econometric Society, New Haven, CT
Zusammenfassung: 
We consider a set of potentially misspecified structural models, geometrically combine their likelihood functions, and estimate the parameters using composite methods. In a Monte Carlo study, composite estimators dominate likelihood-based estimators in mean squared error and composite models are superior to individual models in the Kullback-Leibler sense. We describe Bayesian quasi-posterior computations and compare our approach to Bayesian model averaging, finite mixture, and robust control procedures. We robustify inference using the composite posterior distribution of the parameters and the pool of models. We provide estimates of the marginal propensity to consume and evaluate the role of technology shocks for output fluctuations.
Schlagwörter: 
Bayesian model averaging
composite likelihood
finite mixture
Model misspecification
JEL: 
C13
C51
E17
Persistent Identifier der Erstveröffentlichung: 
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