Hierarchical Bayes forecasts of multinomial Dirichlet data applied to coupon redemptions

Hierarchical Bayes forecasts of multinomial Dirichlet data applied to coupon redemptions

0.00 Avg rating0 Votes
Article ID: iaor19932472
Country: United Kingdom
Volume: 11
Issue: 7
Start Page Number: 603
End Page Number: 619
Publication Date: Nov 1992
Journal: International Journal of Forecasting
Authors:
Keywords: Bayesian forecasting
Abstract:

This paper considers forecasting count data from a multinomial Dirichlet distribution. The forecasting procedure implements hierarchical Bayes methods in order to develop a prior distribution for a new series of data. The methodology is applied to the redemption of cents-off promotional coupons. In a forecasting experiment, early forecasts of new series are similar to those from pooling all redemptions from previous coupon promotions. However, the hierarchical Bayes model provides realistic estimates of forecasting errors, while those for the pooled forecasts are consistently optimistic. As the current series evolves, the hierarchical Bayes forecasts adapt more rapidly and are more accurate than pooled forecasts.

Reviews

Required fields are marked *. Your email address will not be published.