SMOOTH QUANTILE-BASED MODELING OF BRAND SALES, PRICE AND PROMOTIONAL EFFECTS FROM RETAIL SCANNER PANELS

Harry Haupt · Kathrin Kagerer · Winfried J. Steiner

15 citations0/0 files storedJournal of Applied Econometrics

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Consumer Market Behavior and Pricing Supply Chain and Inventory Management Forecasting Techniques and Applications

Abstract

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.

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1995Automobile Prices in Market Equilibrium 2000Mixed MNL models for discrete response 1994Estimating Discrete-Choice Models of Product Differentiation 1989Handbooks in operations research and management science 1995Product Differentiation and Oligopoly in International Markets: The Case of the U.S. Automobile Industry 2000A quantile regression neural network approach to estimating the conditional density of multiperiod returns 1991Market response models: Econometric and time series analysis 1997Creating Micro-Marketing Pricing Strategies Using Supermarket Scanner Data

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2020Interpretable machine learning for demand modeling with high-dimensional data using Gradient Boosting Machines and Shapley values 2018Estimating time-varying parameters in brand choice models: A semiparametric approach

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