Dealing with misspecification in structural macroeconometric models
结构宏观计量模型中的误设处理
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,…