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

Pei-Chun Lai and David Bessler ()
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Pei-Chun Lai: Capital University of Economics and Business

Journal of Applied Economics, 2015, vol. 18, 173-198

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.

Keywords: causal relationships; PC algorithm; Linear Non-Gaussian Acyclic Models (LiNGAM) (search for similar items in EconPapers)
JEL-codes: C1 L1 (search for similar items in EconPapers)
Date: 2015
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Journal of Applied Economics is currently edited by Germán Coloma and Mariana Conte Grand and Jorge M. Streb

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Handle: RePEc:cem:jaecon:v:18:y:2015:n:1:p:173-198