Working Paper: CEPR ID: DP13511
Authors: Fabio Canova; Christian Matthes
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: Model Misspecification; Composite Likelihood; Bayesian Model Averaging; Finite Mixture
JEL Codes: C13; C51; E17
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| technology shocks (D89) | output fluctuations (E39) |
| model specification (C52) | MPC out of transitory income (E19) |
| technology shocks (D89) | MPC out of transitory income (E19) |