We study a many country endogenous growth model in which decisions about innovation and new investment are influenced by growth expectations. Adaptive learning dynamics determine country-specific short run transition paths. Countries differ in basic structural parameters and may impose tariffs...

We consider the stability under adaptive learning of the complete set of solutions to the model when . In addition to the fundamentals solution, the literature describes both finite-state Markov sunspot solutions, satisfying a resonant frequency condition, and autoregressive solutions depending...

We examine the nonlinear model Xt = Et F(xt+1) . Markov SSEs exist near an indeterminate steady state, X = F(X), provided F´(X)> 1. We show that there exist Markov SSEs that are E-stable, and therefore locally stable under adaptive learning, if F´(X)< -1.

An economy exhibits structural heterogeneity when the forecasts of different agents have different effects on the determination of aggregate variables. Various forms of structural heterogeneity can arise and we study the important case of economies in which agents' behavior depends on forecasts...

We develop a monetary model with flexible supply of labor, cash in advance constraints and government spending financed by seignorage. This model has two regimes. One regime is conventional with two steady states. The other regime has a unique steady state which can be determinate or...

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,...

The rational expectations hypothesis swept through macroeconomics during the 1970's and permanently altered the landscape. It remains the prevailing paradigm in macroeconomics, and rational expectations is routinely used as the standard solution concept in both theoretical and applied...

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...