Simple Forecasts and Paradigm Shifts

Abstract

We postulate that agents make forecasts using overly simplified models of the world - i.e., models that only embody a subset of available information. We then go on to study the implications of learning in this environment. Our key premise is that learning is based on a model-selection criterion. Thus if a particular simple model does a poor job of forecasting over a period of time, it is eventually discarded in favor of an alternative, yet equally simple model that would have done better over the same period. This theory makes several distinctive predictions, which, for concreteness, we develop in a stock-market setting. For example, starting with symmetric and homoskedastic fundamentals, the theory yields forecastable variation in the size of the value/glamour differential, in volatility, and in the skewness of returns. Some of these features mirror familiar accounts of stock-price bubbles.