We thank Klaus Adams, Patrick Bajari, Martin Ellison, John Geweke, James Hamilton, Lars Hansen, Sagiri Kitao, Ramon Marimon, Athanasios Orphanides, Martin Schneider, and two referees for useful criticisms and discussions. Sargent thanks the National Science foundation for research support.
Benefits from U.S. Monetary Policy Experimentation in the Days of Samuelson and Solow and Lucas
Version of Record online: 18 JAN 2007
Journal of Money, Credit and Banking
Volume 39, Issue Supplement s1, pages 67–99, February 2007
How to Cite
COGLEY, T., COLACITO, R. and SARGENT, T. J. (2007), Benefits from U.S. Monetary Policy Experimentation in the Days of Samuelson and Solow and Lucas. Journal of Money, Credit and Banking, 39: 67–99. doi: 10.1111/j.1538-4616.2007.00016.x
- Issue online: 18 JAN 2007
- Version of Record online: 18 JAN 2007
- Received October 10, 2005; and accepted in revised form September 1, 2006.
- model uncertainty;
- Bayes' law;
- intentional experimentation;
- Phillips curve;
- Bayesian analysis;
- optimization techniques;
- programming models;
- dynamic analysis;
- central banks and their policies
A policy maker knows two models. One implies an exploitable inflation-unemployment trade-off, the other does not. The policy maker's prior probability over the two models is part of his state vector. Bayes' law converts the prior probability into a posterior probability and gives the policy maker an incentive to experiment. For models calibrated to U.S. data through the early 1960s, we compare the outcomes from two Bellman equations. The first tells the policy maker to “experiment and learn.” The second tells him to “learn but don't experiment.” In this way, we isolate a component of government policy that is due to experimentation and estimate the benefits from intentional experimentation. We interpret the Bellman equation that learns but does not intentionally experiment as an “anticipated utility” model and study how well its outcomes approximate those from the “experiment and learn” Bellman equation. The approximation is good. For our calibrations, the benefits from purposeful experimentation are small because random shocks are big enough to provide ample unintentional experimentation.