TY  - JOUR
AU  - Svensson,Lars E.O.
AU  - Williams,Noah M.
TI  - Bayesian and Adaptive Optimal Policy under Model Uncertainty
JF  - National Bureau of Economic Research Working Paper Series
VL  - No. 13414
PY  - 2007
Y2  - September 2007
UR  - http://www.nber.org/papers/w13414
L1  - http://www.nber.org/papers/w13414.pdf
N1  - Author contact info:
Lars E.O. Svensson
Sveriges Riksbank
SE-103 37 Stockholm
SWEDEN
Tel: +46 8 787 0107
Fax: +46 8 21 0531
E-Mail: lars.svensson@iies.su.se

Noah M. Williams
Department of Economics
1180 Observatory Drive
University of Wisconsin
Madison, WI 53706-1393
Tel: 608/263-3864
E-Mail: nmwilliams@wisc.edu
AB  - We study the problem of a policymaker who seeks to set policy optimally in an economy where the true economic structure is unobserved, and he optimally learns from  observations of the economy. This is a classic problem of learning and control, variants of which have been studied in the past, but seldom 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, computing 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 some simple examples to illustrate the role of learning and experimentation in an MJLQ framework.
ER  -