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Price discovery between carbonated soft drink manufacturers and retailers: A disaggregate analysis with PC and LiNGAM algorithms

Author

Listed:
  • Pei-Chun Lai

    (Capital University of Economics and Business)

  • David A. Bessler

    (Texas A&M University)

Abstract

This paper considers the use of two machine learning algorithms to identify the causal relationships among retail prices, manufacturer prices, and number of packages sold. The two algorithms are PC and Linear Non-Gaussian Acyclic Models (LiNGAM). The dataset studied comprises scanner data collected from the retail sales of carbonated soft drinks in the Chicago area. The PC algorithm is not able to assign direction among retail price, manufacturer price and quantity sold, whereas the LiNGAM algorithm is able to decide in every case, i.e., retail price leads manufacturer price and quantity sold.

Suggested Citation

  • Pei-Chun Lai & David A. Bessler, 2015. "Price discovery between carbonated soft drink manufacturers and retailers: A disaggregate analysis with PC and LiNGAM algorithms," Journal of Applied Economics, Universidad del CEMA, vol. 18, pages 173-198, May.
    • Handle: RePEc:cem:jaecon:v:18:y:2015:n:1:p:173-198
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    File URL: https://ucema.edu.ar/publicaciones/download/volume18/Lai.pdf
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    Citations

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    Cited by:

    1. Xiaojie Xu, 2019. "Contemporaneous Causal Orderings of CSI300 and Futures Prices through Directed Acyclic Graphs," Economics Bulletin, AccessEcon, vol. 39(3), pages 2052-2077.
    2. Huang, Wei & Lai, Pei-Chun & Bessler, David A., 2018. "On the changing structure among Chinese equity markets: Hong Kong, Shanghai, and Shenzhen," European Journal of Operational Research, Elsevier, vol. 264(3), pages 1020-1032.
    3. Xiaojie Xu & Yun Zhang, 2022. "Contemporaneous causality among one hundred Chinese cities," Empirical Economics, Springer, vol. 63(4), pages 2315-2329, October.
    4. Xiaojie Xu, 2017. "Contemporaneous causal orderings of US corn cash prices through directed acyclic graphs," Empirical Economics, Springer, vol. 52(2), pages 731-758, March.
    5. Tejeda, Hernan A. & Kim, Man-Keun, 2020. "Dynamic price relationships and price discovery among cheese markets," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 24(1), September.
    6. Kim, Man-Keun & Tejeda, Hernan & Yu, T. Edward, 2017. "U.S. milled rice markets and integration across regions and types," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 20(5).
    7. Senia, Mark C. & Dharmasena, Senarath & Todd, Jessica E., 2018. "A Complex Model of Consumer Food Acquisitions: Applying Machine Learning and Directed Acyclic Graphs to the National Household Food Acquisition and Purchase Survey (FoodAPS)," 2018 Annual Meeting, February 2-6, 2018, Jacksonville, Florida 266536, Southern Agricultural Economics Association.

    More about this item

    Keywords

    causal relationships; PC algorithm; Linear Non-Gaussian Acyclic Models (LiNGAM);
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance

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