Search Results
Showing results 1 to 7 of approximately 7.
(refine search)
Journal Article
Convergence to Rational Expectations in Learning Models: A Note of Caution
We show in a simple monetary model that the learning dynamics do not converge to the rational expectations monetary steady state. We then show it is necessary to restrict the learning rule to obtain convergence. We derive an upper bound on the gain parameter in the learning rule, based on economic fundamentals in the monetary model, such that gain parameters above the upper bound would imply that the learning dynamics would diverge from the rational expectations monetary steady state.
Journal Article
Stability and Equilibrium Selection in Learning Models: A Note of Caution
Relative to rational expectations models, learning models provide a theory of expectation formation where agents use observed data and a learning rule. Given the possibility of multiple equilibria under rational expectations, the learning literature often uses stability as a criterion to select an equilibrium. This article uses a monetary economy to illustrate that equilibrium selection based on stability is sensitive to specifications of the learning rule. The stability criterion selects qualitatively different equilibria even when the differences in learning specifications are small.
Working Paper
Escapist policy rules
We study a simple, microfounded macroeconomic system in which the monetary authority employs a Taylor-type policy rule. We analyze situations in which the self-confirming equilibrium is unique and learnable according to Bullard and Mitra (2002). We explore the prospects for the use of ?large deviation? theory in this context, as employed by Sargent (1999) and Cho, Williams, and Sargent (2002). We show that our system can sometimes depart from the self-confirming equilibrium towards a non-equilibrium outcome characterized by persistently low nominal interest rates and persistently low in- ...
Working Paper
Convergence to Rational Expectations in Learning Models: A Note of Caution
This paper illustrates a challenge in analyzing the learning algorithms resulting in second-order difference equations. We show in a simple monetary model that the learning dynamics do not converge to the rational expectations monetary steady state. We then show that to guarantee convergence, the gain parameter used in the learning rule has to be restricted based on economic fundamentals in the monetary model.
Working Paper
Discussion of Evans and Honkapohja, \"Policy interaction, expectations, and the liquidity trap\"
The result of Benhabib, Schmitt-Groh, and Uribe (2001) is powerful because it relies only on three rather natural conditions: the Fisher equation, the convex Taylor rule, and the lower bound of the nominal interest rate. Their result is striking because the paper reveals the peril of the active Taylor rule, which has been shown to implement the target in a stable manner under various conditions. In a related paper, Benhabib, Schmitt-Groh, and Uribe (2002) proposed a number of policies designed to avoid the liquidity trap outcome. One is to link government's spending to the inflation rate. ...
Journal Article
Model Averaging and Persistent Disagreement
The authors consider the following scenario: Two agents construct models of an endogenous price process. One agent thinks the data are stationary, the other thinks the data are nonstationary. A policymaker combines forecasts from the two models using a recursive Bayesian model averaging procedure. The actual (but unknown) price process depends on the policymaker?s forecasts. The authors find that if the policymaker has complete faith in the stationary model, then beliefs and outcomes converge to the stationary rational expectations equilibrium. However, even a grain of doubt about ...
Working Paper
Convergence to Rational Expectations in Learning Models: A Note of Caution
This paper illustrates a challenge in analyzing the learning algorithms resulting in second-order difference equations. We show in a simple monetary model that the learning dynamics do not converge to the rational expectations monetary steady state. We then show that to guarantee convergence, the gain parameter used in the learning rule has to be restricted based on economic fundamentals in the monetary model.