Article ID: | iaor1996669 |
Country: | United Kingdom |
Volume: | 14 |
Issue: | 3 |
Start Page Number: | 181 |
End Page Number: | 199 |
Publication Date: | May 1995 |
Journal: | International Journal of Forecasting |
Authors: | Curry David J., Divakar Suresh, Mathur Sharat K., Whiteman Charles H. |
Keywords: | marketing |
Categroy management-a relatively new function in marketing-involves large-scale, real-time forecasting of multiple data series in complex environments. This paper illustrates how Bayesian Vector Autoregression (BVAR) fulfils the category manager’s decision-support requirements by providing accurate forecasts of a category’s state variables (prices, volumes and advertising levels), incorporating management interventions (merchandising events such as end-aisle displays), and revealing competitive dynamics through impulse response analyses. Using 124 weeks of point-of-sale scanner data comprising 31 variables for four brands, we compare the out-of-sample forecasts from BVAR to forecasts from exponential smoothing, univariate and multivariate Box-Jenkins transfer function analyses, and multivariate ARMA models. Theil