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Prediction in Economic Networks

Author

Listed:
  • Vasant Dhar

    (Stern School of Business, New York University, New York, New York 10012)

  • Tomer Geva

    (Recanati Business School, Tel Aviv University, Tel Aviv 6997801 Israel)

  • Gal Oestreicher-Singer

    (Recanati Business School, Tel Aviv University, Tel Aviv 6997801 Israel)

  • Arun Sundararajan

    (Stern School of Business, New York University, New York, New York 10012)

Abstract

We define an economic network as a linked set of entities, where links are created by actual realizations of shared economic outcomes between entities. We analyze the predictive information contained in a specific type of economic network, namely, a product network, where the links between products reflect aggregated information on the preferences of large numbers of individuals to co-purchase pairs of products. The product network therefore reflects a simple “smoothed” model of demand for related products. Using a data set containing more than 70 million observations of a nonstatic co-purchase network over a period of two years, we predict network entities' future demand by augmenting data on their historical demand with data on the demand for their immediate neighbors, in addition to network properties, specifically, local clustering and PageRank. To our knowledge, this is the first study of a large-scale dynamic network that shows that a product network contains useful distributed information for demand prediction. The economic implications of algorithmically predicting demand for large numbers of products are significant.

Suggested Citation

  • Vasant Dhar & Tomer Geva & Gal Oestreicher-Singer & Arun Sundararajan, 2014. "Prediction in Economic Networks," Information Systems Research, INFORMS, vol. 25(2), pages 264-284, June.
    • Handle: RePEc:inm:orisre:v:25:y:2014:i:2:p:264-284
    DOI: 10.1287/isre.2013.0510
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    References listed on IDEAS

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    9. Wuyue (Phoebe) Shangguan & Alvin Chung Man Leung & Ashish Agarwal & Prabhudev Konana & Xi Chen, 2022. "Developing a Composite Measure to Represent Information Flows in Networks: Evidence from a Stock Market," Information Systems Research, INFORMS, vol. 33(2), pages 413-428, June.
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