Dealing with misspecification in structural macroeconometric models

Artikel

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Journal: Quantitative Economics ; ISSN: 1759-7331 ; Volume: 12 ; Year: 2021 ; Issue: 2 ; Pages: 313-350 ; New Haven, CT: The Econometric Society

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Estimation: General ; Model Construction and Estimation ; General Aggregative Models: Forecasting and Simulation: Models and Applications
• Thema: Bayesian model averaging ; composite likelihood ; finite mixture ; Model misspecification

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Canova, Fabio ; Matthes, Christian
• Ereignis: Veröffentlichung
• (wer): The Econometric Society
• (wo): New Haven, CT
• (wann): 2021

Weitere Informationen

• DOI: doi:10.3982/QE1413
• Handle: http://hdl.handle.net/10419/253597
• Letzte Aktualisierung: 10.03.2025, 11:43 MEZ