Suchergebnisse

Recent papers have analyzed how adaptive agents may converge to and escape from self-confirming equilibria. All of these papers have imputed to agents a particular prior about drifting coefficients. In the context of a model of monetary policy, this paper analyzes dynamics that govern both convergence and escape under a more general class of priors for the government. The authors characterize how the shape of the prior influences the dynamics in important ways. There are priors for which the E-stability condition is not enough to assure local convergence to a self-confirming equilibrium. Their analysis also tracks down the source of differences in the sustainability of Ramsey inflation encountered in the analyses of Sims (1988) and Chung (1990), on the one hand, and Cho, Williams, and Sargent (2002), on the other.

Mean dynamics govern convergence to rational expectations equilibria of self-referential systems under least squares learning. We highlight escape dynamics that propel away from a rational expectations equilibrium under fixed-gain recursive learning schemes. These learning schemes discount past observations. In a model with a unique self-confirming equilibrium, we show that the destination of the escape dynamics is an outcome associated with government discovery of too strong a version of the natural rate hypothesis. That destination is not sustainable as a self-confirming equilibrium but is visited recurrently. The escape route dynamics cause recurrent outcomes close to the Ramsey (commitment) inflation rate in a model with an adaptive government.