Some Implications of Learning for Price Stability
Author(s) -
Stefano Eusepi,
Marc Giani,
Bruce Preston
Publication year - 2017
Publication title -
ssrn electronic journal
Language(s) - English
Resource type - Journals
ISSN - 1556-5068
DOI - 10.2139/ssrn.2901751
Subject(s) - stability (learning theory) , economics , computer science , econometrics , mathematical economics , psychology , machine learning
Survey data on expectations of a range of macroeconomic variables exhibit low-frequency drift. In a New Keynesian model consistent with these empirical properties, optimal policy in general delivers a positive inflation rate in the long run. Two special cases deliver classic outcomes under rational expectations: as the degree of low-frequency variation in beliefs goes to zero, the long-run inflation rate coincides with the inflation bias under optimal discretion; for non-zero low-frequency drift in beliefs, as households become highly patient valuing utility in any period equally, the optimal long-run inflation rate coincides with optimal commitment – price stability is optimal. The optimal state-contingent response to cost-push disturbances similarly reflects properties of optimal discretion and optimal commitment, depending on the degree of low-frequency variation in beliefs. When beliefs exhibit substantial variation in response to short-run forecast errors, optimal policy is closer to commitment.
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