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The authors confront model misspecifications in macroeconomics by proposing an analytic framework for merging multiple models. This framework allows them to address uncertainty about models and parameters simultaneously and trace out the historical periods in which one model dominates other models. They apply the framework to a richly parameterized Dynamic Stochastic General Equilibrium (DSGE) model and a corresponding Bayesian vector autoregressive model. The merged model, fitting the data better than both individual models, substantially alters economic inferences about the DSGE parameters and the implied impulse responses.
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