Dealing with misspecification in structural macroeconometric models

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.

Automated summary

The study examines a set of potentially misspecified structural models, combines their likelihood functions geometrically, and estimates parameters using composite methods. In Monte Carlo simulations, composite estimators outperform likelihood-based estimators in mean squared error, and composite models are superior to individual models in the Kullback-Leibler sense. The study also discusses Bayesian quasi-posterior computations, comparing the approach to Bayesian model averaging, finite mixture, and robust control procedures.