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Seasonality on non-linear price effects in scanner-data based market-response models

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  • Fok, D.
  • Franses, Ph.H.B.F.

Abstract

Scanner data for fast moving consumer goods typically amount to panels of time series where both N and T are large. To reduce the number of parameters and to shrink parameters towards plausible and interpretable values, multi-level models turn out to be useful. Such models contain in the second level a stochastic model to describe the parameters in the first level. In this paper we propose such a model for weekly scanner data where we explicitly address (i) weekly seasonality in a limited number of yearly data and (ii) non-linear price effects due to historic reference prices. We discuss representation and inference and we propose an estimation method using Bayesian techniques. An illustration to a market-response model for 96 brands for about 8 years of weekly data shows the merits of our approach.

Suggested Citation

  • Fok, D. & Franses, Ph.H.B.F., 2005. "Seasonality on non-linear price effects in scanner-data based market-response models," Econometric Institute Research Papers EI 2005-45, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:7032
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    References listed on IDEAS

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    1. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654.
    2. Hendricks, Wallace & Koenker, Roger & Poirier, Dale J., 1979. "Residential demand for electricity : An econometric approach," Journal of Econometrics, Elsevier, vol. 9(1-2), pages 33-57, January.
    3. Granger, Clive W. J. & Terasvirta, Timo, 1993. "Modelling Non-Linear Economic Relationships," OUP Catalogue, Oxford University Press, number 9780198773207.
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    Cited by:

    1. Yuri Peers & Dennis Fok & Philip Hans Franses, 2012. "Modeling Seasonality in New Product Diffusion," Marketing Science, INFORMS, vol. 31(2), pages 351-364, March.
    2. Qi Feng & Sirong Luo & Dan Zhang, 2014. "Dynamic Inventory–Pricing Control Under Backorder: Demand Estimation and Policy Optimization," Manufacturing & Service Operations Management, INFORMS, vol. 16(1), pages 149-160, February.
    3. Guidolin, Mariangela & Guseo, Renato, 2014. "Modelling seasonality in innovation diffusion," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 33-40.
    4. Haupt, Harry & Kagerer, Kathrin, 2012. "Beyond mean estimates of price and promotional effects in scanner-panel sales–response regression," Journal of Retailing and Consumer Services, Elsevier, vol. 19(5), pages 470-483.
    5. Rishab Guha & Serena Ng, 2019. "A Machine Learning Analysis of Seasonal and Cyclical Sales in Weekly Scanner Data," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 403-436, National Bureau of Economic Research, Inc.
    6. Venera Timiryanova & Irina Lakman & Vadim Prudnikov & Dina Krasnoselskaya, 2022. "Spatial Dependence of Average Prices for Product Categories and Its Change over Time: Evidence from Daily Data," Forecasting, MDPI, vol. 5(1), pages 1-25, December.
    7. Wolters, Jannik & Huchzermeier, Arnd, 2021. "Joint In-Season and Out-of-Season Promotion Demand Forecasting in a Retail Environment," Journal of Retailing, Elsevier, vol. 97(4), pages 726-745.

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