In this paper we consider regression models with forecast feedback. Agents' expectations are formed via the recursive estimation of the parameters in an auxiliary model. The learning scheme employed by the agents belongs to the class of stochastic approximation algorithms whose gain sequence is...

We characterize axiomatically a stochastic choice model, the consistent-mistakes model (CMM), that describes an error-prone decision maker's choices. In contrast to random utility models, CMMs generate closed-form choice probability. Under the axioms, we uniquely identify from the choices an...

In this article, we derive a solution for a linear stochastic model on a complex time domain. In this type of models, the time domain can be any collection of points along the real number line, so these models are suitable for problems where events do not occur at evenly-spaced time intervals....

In this paper we present a method for using rational expectations in a linear-quadratic optimizationframework. Following the approach put forward by Sims, we solve the model through a QZdecomposition, which is generally easier to implement than the more widely used method of Blanchardand Kahn.

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,...