Decision Sciences Journal
Volume 32, Number 3
Summer 2001
An Approximate Bayesian Algorithm for Combining Forecasts
Kim-Hung Li
Department of Statistics, The Chinese University of Hong Kong,
Shatin, New Territories, Hong Kong
Heung Wong
Department of Applied Mathematics, The Hong Kong Polytechnic
University, Hung Hom, Kowloon, Hong Kong
Marvin Troutt
Graduate School of Management, Kent State University, Kent, OH
44242-0001
ABSTRACT. In this paper we propose a consensus forecasting
method based on a convex combination of individual forecast densities.
The exact Bayesian updating of the convex combination weights
is very complex and practically prohibitive. We propose a simple
sequential updating alternative method based on function approximation.
Several examples illustrate the method.
Subject Areas: Financial Models, Statistics, Term Structure,
and Time Series Forecasting. |