Robust learning stability with operational monetary policy rules
The recent literature examines the conduct of monetary policy in terms of interest rate rules from the viewpoint of imperfect knowledge and learning by economic agents. The stability of the rational expectations equilibrium is taken as a key desideratum for good monetary policy design. Most of this literature postulates that agents use least squares or related learning algorithms to carry out real-time estimations of the parameters of their forecast functions as new data become available. Moreover, it is usually assumed that the learning algorithms have a decreasing gain, in the most common case, the gain is the inverse of the sample size so that all data points have equal weights. Use of such a decreasing-gain algorithm makes it possible for learning to converge exactly to the rational expectations equilibrium in environments without structural change. Convergence requires that the equilibrium satisfies a stability condition, known as E-stability.