Bayesian prediction with linear dynamic model: principle and application

Li, Y. , Moutinho, L., Opong, K. K. and Pang, Y. (2015) Bayesian prediction with linear dynamic model: principle and application. In: Moutinho, L. and Huarng, K.-H. (eds.) Quantitative Modelling in Marketing and Management [2nd ed.]. World Scientific: Hackensack, NJ, pp. 323-342. ISBN 9789814696340 (doi: 10.1142/9789814696357_0013)

Abstract

In the business applications where only a few data is observed, statistical models estimated in frequentist framework is not reliable or even not obtainable. Bayesian updating, by calculating subjective probabilities conditional on real observations, could form optimal prediction given some prior belief. Through a demonstration of cash flow prediction example, the Bayesian method and a frequentist method, ordinary least square (OLS) to be specific, are compared. Bayesian model has similar performance as OLS in the example and moreover provides a solution to the situations where OLS is inapplicable. Read More: http://www.worldscientific.com/doi/abs/10.1142/9789814696357_0013

• Item Type: Book Sections
• Status: Published
• Glasgow Author(s) Enlighten ID: Pang, Mr Yang and Opong, Professor Kwaku and Moutinho, Professor Luiz and Li, Professor Yun
• Authors: Li, Y., Moutinho, L., Opong, K. K., and Pang, Y.
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy; College of Social Sciences > Adam Smith Business School > Accounting and Finance; College of Social Sciences > Adam Smith Business School > Management
• Publisher: World Scientific
• ISBN: 9789814696340