LEARNING AND EFFICIENCY IN A GAMBLING MARKET

We present a statistical model which uses data on National Football Le ague games and betting lines to study how agents learn from past outco mes and to test market efficiency. Using Kalman Filter estimation, we show that teams' abilities exhibit substantial week-to-week variation during the season. This provides an ideal environment in which to stud y how agents learn from past information. While we do not find strong evidence of market inefficiency, we are able to make several observati ons on market learning. In particular, agents have more difficulty lea rning from ''noisy'' observations and appear to weight recent observat ions less than our statistical model suggests is optimal.