We introduce the E-correspondence principle for stochastic dynamic expectations models as a tool for comparative dynamics analysis. The principle is applicable to equilibria that are stable under least squares and closely related learning rules. With this technique it is possible to study,...

We introduce the E-correspondence principle for stochastic dynamic expectations models as a tool for comparative dynamics analysis. The principle is applicable to equilibria that are stable under least squares and closely related learning rules. With this technique it is possible to study,...

We review the recent work on interest rate setting, which emphasizes the desirability of designing policy to ensure stability under private agent learning.Appropriately designed expectations based rules can yield optimal rational expectations equilibria that are both determinate and stable under...

We consider the robust stability of a rational expectations equilibrium, which we define as stability under discounted (constant gain) least-squares learning, for a range of gain parameters. We find that for operational forms of policy rules, ie rules that do not depend on contemporaneous values...

We study the properties of generalized stochastic gradient (GSG) learning in forward-looking models. We examine how the conditions for stability of standard stochastic gradient (SG) learning both differ from and are related to E-stability, which governs stability under least squares learning. SG...

In an asset-pricing model, risk-averse agents need to forecast the conditional variance of a stock's return. A near-rational restricted perceptions equilibrium exists in which agents believe prices follow a random walk with a conditional variance that is self-fulfilling. When agents estimate...