Herding in Probabilistic Forecasts

60 Pages Posted: 1 Oct 2020

See all articles by Yanwei Jia

Yanwei Jia

Columbia University - Department of Industrial Engineering and Operations Research

Jussi Keppo

National University of Singapore - NUS Business School

Ville Satopää

INSEAD - Technology and Operations Management

Date Written: August 16, 2020

Abstract

Decision and policy makers often ask experts to forecast a future state. Experts, however, can be biased. In the economics and psychology literature, one extensively studied behavioral bias is called herding. Under strong levels of herding, it is generally known that disclosure of public information may lower forecasting accuracy. This result, however, has been derived only for point forecasts. In this paper, we consider probabilistic forecasts under herding and show that the negative externality of public information no longer holds. Furthermore, we show that the experts report too similar locations and inflate the variance of their forecasts due to herding. In addition to reacting to new information as expected, probabilistic forecasts contain more information about the experts’ full beliefs and interpersonal structure. This facilitates model estimation. To this end, we consider a one-shot setting with one forecast per expert and show that our model is identifiable up to an infinite number of solutions based on point forecasts, but up to two solutions based on probabilistic forecasts. We then provide a Bayesian estimation procedure for these two solutions and apply it to economic forecasting data collected by the European Central Bank. We find that, on average, the experts invest around 15% of their efforts into making similar forecasts. The level of herding shows an increasing trend from 1999 to 2007 but drops sharply during the financial crisis of 2007-2008, and then rises again until 2019.

Keywords: Asymmetric Information Game, Bayesian Statistics, Economic Forecasting, Public Disclosure

Suggested Citation

Jia, Yanwei and Keppo, Jussi and Satopää, Ville, Herding in Probabilistic Forecasts (August 16, 2020). Available at SSRN: https://ssrn.com/abstract=3674961 or http://dx.doi.org/10.2139/ssrn.3674961

Yanwei Jia

Columbia University - Department of Industrial Engineering and Operations Research ( email )

500 W. 120th Street #315
New York, NY 10027
United States

Jussi Keppo

National University of Singapore - NUS Business School ( email )

1 Business Link
Singapore, 117592
Singapore

Ville Satopää (Contact Author)

INSEAD - Technology and Operations Management ( email )

Boulevard de Constance
77 305 Fontainebleau Cedex
France

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
143
Abstract Views
697
rank
249,930
PlumX Metrics