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Dynamic Pricing Recognition on E-Commerce Platforms with VAR Processes

Author

Listed:
  • Alexander Faehnle

    (Alten Italia, 35131 Padua, Italy)

  • Mariangela Guidolin

    (Department of Statistical Sciences, University of Padua, 35121 Padua, Italy)

Abstract

In an environment such as e-commerce, characterized by the presence of numerous agents, competition based on product characteristics is a very important aspect. This paper proposes a model based on vector autoregressive processes (VAR) and Lasso penalization to detect and examine the dynamics that govern real-time price competition in electronic marketplaces. Employing this model, an empirical study was performed on the price trends of smartphone models on the major electronic sales platforms of the Italian market. The proposed model detects real-time price variations in single vendors, based on the variations of their direct competitors. The statistical method adopted in this analysis may be useful for e-commerce companies that conduct market analyses of competitors’ pricing strategies.

Suggested Citation

  • Alexander Faehnle & Mariangela Guidolin, 2021. "Dynamic Pricing Recognition on E-Commerce Platforms with VAR Processes," Forecasting, MDPI, vol. 3(1), pages 1-15, March.
    • Handle: RePEc:gam:jforec:v:3:y:2021:i:1:p:11-180:d:510917
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    References listed on IDEAS

    as
    1. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    2. Takada, Hirokazu & Bass, Frank M., 1998. "Multiple Time Series Analysis of Competitive Marketing Behavior," Journal of Business Research, Elsevier, vol. 43(2), pages 97-107, October.
    3. Ma, Shaohui & Fildes, Robert & Huang, Tao, 2016. "Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information," European Journal of Operational Research, Elsevier, vol. 249(1), pages 245-257.
    4. Cheung, Yin-Wong & Lai, Kon S, 1995. "Lag Order and Critical Values of a Modified Dickey-Fuller Test," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 57(3), pages 411-419, August.
    5. Cheung, Yin-Wong & Lai, Kon S, 1995. "Lag Order and Critical Values of the Augmented Dickey-Fuller Test," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 277-280, July.
    6. Gelper, Sarah & Wilms, Ines & Croux, Christophe, 2016. "Identifying Demand Effects in a Large Network of Product Categories," Journal of Retailing, Elsevier, vol. 92(1), pages 25-39.
    7. Hsu, Nan-Jung & Hung, Hung-Lin & Chang, Ya-Mei, 2008. "Subset selection for vector autoregressive processes using Lasso," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3645-3657, March.
    8. Franses, Philip Hans, 2006. "Forecasting in Marketing," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 18, pages 983-1012, Elsevier.
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    Cited by:

    1. Alessia Paccagnini, 2021. "Editorial for Special Issue “New Frontiers in Forecasting the Business Cycle and Financial Markets”," Forecasting, MDPI, vol. 3(3), pages 1-3, July.
    2. Jakob Korbel & Marc Riar & Thorsten Pröhl & Rüdiger Zarnekow, 2024. "What drives user interest and purchase of virtual 3D assets? An empirical investigation of 3D model attributes and pricing dynamics," Post-Print hal-04833944, HAL.

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