SMOOTH QUANTILE-BASED MODELING OF BRAND SALES, PRICE AND PROMOTIONAL EFFECTS FROM RETAIL SCANNER PANELS
Article first published online: 7 OCT 2013
Copyright © 2013 John Wiley & Sons, Ltd.
Journal of Applied Econometrics
Volume 29, Issue 6, pages 1007–1028, September/October 2014
How to Cite
2014), SMOOTH QUANTILE-BASED MODELING OF BRAND SALES, PRICE AND PROMOTIONAL EFFECTS FROM RETAIL SCANNER PANELS, J. Appl. Econ., 29, 1007–1028, doi: 10.1002/jae.2347, , and (
- Issue published online: 8 OCT 2014
- Article first published online: 7 OCT 2013
- Manuscript Revised: 24 JUN 2013
- Manuscript Received: 21 NOV 2011
Semiparametric quantile regression is employed to flexibly estimate sales response for frequently purchased consumer goods. Using retail store-level data, we compare the performance of models with and without monotonic smoothing for fit and prediction accuracy. We find that (a) flexible models with monotonicity constraints imposed on price effects dominate both in-sample and out-of-sample comparisons while being robust even at the boundaries of the price distribution when data is sparse; (b) quantile-based confidence intervals are much more accurate compared to least-squares-based intervals; (c) specifications reflecting that managers may not have exact knowledge about future competitive pricing perform extremely well. Copyright © 2013 John Wiley & Sons, Ltd.