Consistent Estimation of Structural Parameters in Regression Models with Adaptive Learning

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 decreasing to zero. Our focus is on the estimation of the parameters in the resulting actual law of motion. For a special case we show that the ordinary least squares estimator is consistent.