Prediction in Economic Networks

Vasant Dhar (), Tomer Geva (), Gal Oestreicher-Singer () and Arun Sundararajan ()
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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

Information Systems Research, 2014, vol. 25, issue 2, 264-284

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.

Keywords: economic networks; prediction; co-purchase network; predictive modeling; neural networks; autoregressive models; network-based prediction; PageRank (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orisre:v:25:y:2014:i:2:p:264-284

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