by
Jussi Keppo, National University of Singapore
Wednesday, March 13, 2019 | 2:00pm-3:30pm | Room 335, HSBC Business School Building
Abstract
This article analyzes multiple experts who forecast an underlying dynamic state based on a stream of public and private signals. Each expert forecaster minimizes a convex combination of her forecasting error and deviation from the other experts' forecasts. As a result, the experts exhibit herding behavior -- a bias that has been well-recognized in the economics and psychology literature. Our first contribution derives and analyzes the experts' optimal forecast under different levels of herding. This extends the Kalman filter to applications where herding is an important part of the process. Our second contribution is a welfare analysis where we show that, on average, the precision of public information affects welfare more than the level of herding among the experts. However, on average, the level of herding decreases the heterogeneity in the experts' forecasts more than the precision of public information. Our third contribution is an estimation scheme for our model and a resulting simple compensation scheme that minimizes the herding effect.
