Keywords: Monetary policy, learning
Abstract: The monetary policy rules that are widely discussed--notably the
Taylor rule--are remarkable for their simplicity. One reason for
the apparant preference for simple ad hoc rules over optimal rules
might be the assumption of full information maintained in the
computation of an optimal rule. Arguably this makes optimal control
rules less robust to model specification errors. In this paper,
we drop the full-information assumption and investigate the choice
of policy rules when agents must learn the rule that is in use.
To do this, we conduct stochastic simulations on a small, estimated
forward-looking model, with agents following a strategy of least-
squares learning or discounted least-squares learning. We find that
the costs of learning a new rule can, under some circumstances,
be substantial. These circumstances vary with the preferences
of the monetary authority and with the rule initially in place.
Policymakers with strong preferences for inflation control must
incur substantial costs when they change the rule; but they are
nearly always willing to bear those costs. Policymakers with weak
preferences for inflation control, on the other hand, may actually
benefit from agents' prior belief that a strong rule is in
Full paper (3481 KB PDF)
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Last update: March 19, 1999