Learning About Monetary Policy Rules

We study macroeconomic systems with forward-looking private sector agents and a monetary authority that is trying to control the economy through the use of a linear policy feedback rule. A typical finding in the burgeoning literature in this area is that policymakers should be relatively aggressive in responding to available information about the macroeconomy. A natural question to ask about this result is whether policy responses which are too aggressive might actually destabilize the economy. We use stability under recursive learning (a la Evans and Honkapohja (2000)) as a criterion for evaluating monetary policy rules in this context. We find that considering learning can alter the evaluation of alternative policy rules.