Dealing with Misspecification in Structural Macroeconometric Models

51 Pages Posted: 11 Feb 2019 Last revised: 18 Feb 2019

See all articles by Fabio Canova

Fabio Canova

Bi norwegian business school

Christian Matthes

Federal Reserve Bank of Richmond

Date Written: February 2019

Abstract

We consider a set of potentially misspecified structural models, geometrically combine their likelihood functions, and estimate the parameters using composite methods. Composite estimators may be preferable to likelihood-based estimators in the mean squared error.

Composite models may be superior to individual models in the Kullback-Leibler sense. We describe Bayesian quasi-posterior computations and compare the approach to Bayesian model averaging, finite mixture methods, and robustness 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.

Keywords: Bayesian model averaging, composite likelihood, finite mixture, model misspecification

JEL Classification: C13, C51, E17

Suggested Citation

Canova, Fabio and Matthes, Christian, Dealing with Misspecification in Structural Macroeconometric Models (February 2019). CEPR Discussion Paper No. DP13511. Available at SSRN: https://ssrn.com/abstract=3332314

Fabio Canova (Contact Author)

Bi norwegian business school ( email )

Nydalsveien 37
Oslo, 0484
Norway

Christian Matthes

Federal Reserve Bank of Richmond ( email )

P.O. Box 27622
Richmond, VA 23261
United States

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