The authors propose a new methodology for deriving hierarchical product-market structures from disaggregate purchase data. A hierarchical product-market tree is estimated from scanner panel purchase data in a maximum likelihood framework. The derived product-market representation portrays both products and market segments as terminal nodes in a hierarchical tree where the “closer” a product is to a particular segment, the higher its revealed preference for that product. The hierarchical representation of products and segments as well as the composition of the market segments are derived simultaneously. An empirical application examines the descriptive and predictive validity of the methodology.
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