Bayesian and adaptive optimal policy under model uncertainty

We study the problem of a policymaker who seeks to set policy optimally in an economy where the true economic structure is unobserved, and policymakers optimally learn from their observations of the economy. This is a cl
We study the problem of a policymaker who seeks to set policy optimally in an economy where the true economic structure is unobserved, and policymakers optimally learn from their observations of the economy. This is a classic problem of learning and control, variants of which have been studied in the past, but little with forward-looking variables which are a key component of modern policy-relevant models. As in most Bayesian learning problems, the optimal policy typically includes an experimentation component reflecting the endogeneity of information. We develop algorithms to solve numerically for the Bayesian optimal policy (BOP). However the BOP is only feasible in relatively small models, and thus we also consider a simpler specification we term adaptive optimal policy (AOP) which allows policymakers to update their beliefs but shortcuts the experimentation motive. In our setting, the AOP is significantly easier to compute, and in many cases provides a good approximation to the BOP. We provide a simple example to illustrate the role of learning and experimentation in an MJLQ framework. JEL Classification: E42, E52, E58
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Metadaten
Author:Lars E.O. Svensson, Noah Williams
URN:urn:nbn:de:hebis:30-38218
Series (Serial Number):CFS working paper series (2007, 11)
Document Type:Working Paper
Language:English
Date of Publication (online):2007/02/23
Year of first Publication:2006
Publishing Institution:Univ.-Bibliothek Frankfurt am Main
Release Date:2007/02/23
Tag:Learning ; Optimal Monetary Policy ; Recursive Saddlepoint Method
Note:
Version November 2006
HeBIS PPN:190111569
Institutes:Center for Financial Studies (CFS)
Dewey Decimal Classification:330 Wirtschaft
Sammlungen:Universitätspublikationen
Licence (German):License Logo Veröffentlichungsvertrag für Publikationen

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