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Information-Consistent Learning and Shifts in Long-Run Productivity
With Neville Francis
- Recent work in the macroeconomic literature has incorporated learning into DSGE models in an inconsistent manner. Particularly, agents are assumed to learn and update forecasts with a less complete information set than they use to detrend and make optimal decisions. We present an information-consistent learning framework in which the information set is the same in every phase of the model. We further illustrate the effects of violating information-consistency within the basic RBC framework.
