IDEAS home Printed from https://ideas.repec.org/a/wly/japmet/v29y2014i6p1007-1028.html
   My bibliography  Save this article

Smooth Quantile‐Based Modeling Of Brand Sales, Price And Promotional Effects From Retail Scanner Panels

Author

Listed:
  • Harry Haupt
  • Kathrin Kagerer
  • Winfried J. Steiner

Abstract

SUMMARY 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.

Suggested Citation

  • Harry Haupt & Kathrin Kagerer & Winfried J. Steiner, 2014. "Smooth Quantile‐Based Modeling Of Brand Sales, Price And Promotional Effects From Retail Scanner Panels," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(6), pages 1007-1028, September.
    • Handle: RePEc:wly:japmet:v:29:y:2014:i:6:p:1007-1028
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Guhl, Daniel & Baumgartner, Bernhard & Kneib, Thomas & Steiner, Winfried J., 2018. "Estimating time-varying parameters in brand choice models: A semiparametric approach," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 394-414.
    2. Bernhard Baumgartner & Daniel Guhl & Thomas Kneib & Winfried J. Steiner, 2018. "Flexible estimation of time-varying effects for frequently purchased retail goods: a modeling approach based on household panel data," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(4), pages 837-873, October.
    3. Weber, Anett & Steiner, Winfried J., 2021. "Modeling price response from retail sales: An empirical comparison of models with different representations of heterogeneity," European Journal of Operational Research, Elsevier, vol. 294(3), pages 843-859.
    4. Hoeltgebaum, Henrique & Borenstein, Denis & Fernandes, Cristiano & Veiga, Álvaro, 2021. "A score-driven model of short-term demand forecasting for retail distribution centers," Journal of Retailing, Elsevier, vol. 97(4), pages 715-725.
    5. Shujie Ma & Jeffrey S. Racine & Aman Ullah, 2015. "Nonparametric Regression-Spline Random Effects Models," Department of Economics Working Papers 2015-10, McMaster University.
    6. Sagaert, Yves R. & Aghezzaf, El-Houssaine & Kourentzes, Nikolaos & Desmet, Bram, 2018. "Tactical sales forecasting using a very large set of macroeconomic indicators," European Journal of Operational Research, Elsevier, vol. 264(2), pages 558-569.
    7. Philipp Aschersleben & Winfried J. Steiner, 2022. "A semiparametric approach to estimating reference price effects in sales response models," Journal of Business Economics, Springer, vol. 92(4), pages 591-643, May.
    8. Lang, Stefan & Steiner, Winfried J. & Weber, Anett & Wechselberger, Peter, 2015. "Accommodating heterogeneity and nonlinearity in price effects for predicting brand sales and profits," European Journal of Operational Research, Elsevier, vol. 246(1), pages 232-241.
    9. Ulrich, Matthias & Jahnke, Hermann & Langrock, Roland & Pesch, Robert & Senge, Robin, 2021. "Distributional regression for demand forecasting in e-grocery," European Journal of Operational Research, Elsevier, vol. 294(3), pages 831-842.
    10. Evgeny A. Antipov & Elena B. Pokryshevskaya, 2020. "Interpretable machine learning for demand modeling with high-dimensional data using Gradient Boosting Machines and Shapley values," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(5), pages 355-364, October.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:japmet:v:29:y:2014:i:6:p:1007-1028. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/0883-7252/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.