Bayesian and adaptive optimal policy under model uncertainty

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: CFS Working Paper ; No. 2007/11

Classification and Topics

• Classification: Wirtschaft ; Monetary Systems; Standards; Regimes; Government and the Monetary System; Payment Systems ; Monetary Policy ; Central Banks and Their Policies
• Subject: Optimal Monetary Policy ; Learning ; Recursive Saddlepoint Method ; Politische Entscheidung ; Entscheidung bei Unsicherheit ; Lernen ; Informationsbeschaffung ; Theorie

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Svensson, Lars E. O. ; Williams, Noah
• Event: Veröffentlichung
• (who): Goethe University Frankfurt, Center for Financial Studies (CFS)
• (where): Frankfurt a. M.
• (when): 2006

Further information

• Handle: http://hdl.handle.net/10419/25512
• URN: urn:nbn:de:hebis:30-38218
• Last update: 10.03.2025, 11:44 AM CET