Benefits from U.S. Monetary Policy Experimentation in the Days of Samuelson and Solow and Lucas

A policy maker knows two models of inflation-unemployment dynamics. One implies an exploitable 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 into a posterior at each date and gives the policy maker an incentive to experiment. For a model calibrated to U.S. data through the early 1960s, we isolate the component of government policy that is due to experimentation by comparing the outcomes from two Bellman equations, the first of which embodies a `experiment and learn' setup, the second of which embodies a `don't experiment, do learn' view. We interpret the second as an example of an `anticipated utility' model and study how well its outcomes approximate those from the `experiment and learn' Bellman equation