We consider a simple market environment in which traders with finite memory update forecasting rules at random intervals by OLS. In this context, changes in the perception of market risk can trigger volatility and bubbles. Consequently, higher degrees of risk response among traders can have a...

We consider an environment in which traders with finite memory update their forecast rules at random intervals by OLS. In this context, overparameterization of the forecast rules can destabilize the learning dynamics. This instability tends to be attenuated by greater memory and less frequent...

We explore the consequence of learning to forecast in a very simple environment. Agents have bounded memory and incorrectly believe that there is nonlinear structure underlying the aggregate time series dynamics. Under social learning with finite memory, agents may be unable to learn the true...