Avoiding aggregation bias in demand estimation: A multivariate promotional disaggregation approach

Steven Tenn ()

Quantitative Marketing and Economics (QME), 2006, vol. 4, issue 4, 383-405

Abstract: Demand models produce biased results when applied to data aggregated across stores with heterogeneous promotional activity. We show how to modify extant aggregate demand frameworks to avoid this problem. First a consumer-level model is developed, which is then integrated over the heterogeneous stores to arrive at aggregate demand. Our approach is highly practical since it requires only standard scanner data of the type produced by the major vendors. Using data for super-premium ice cream, we apply the proposed methodology to the random coefficients logit demand framework. Copyright Springer Science + Business Media, LLC 2006

Keywords: Aggregation bias; Demand estimation; Scanner data (search for similar items in EconPapers)
Date: 2006
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://hdl.handle.net/10.1007/s11129-006-9011-3 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:qmktec:v:4:y:2006:i:4:p:383-405

Ordering information: This journal article can be ordered from
http://www.springer. ... ng/journal/11129/PS2

DOI: 10.1007/s11129-006-9011-3

Access Statistics for this article

Quantitative Marketing and Economics (QME) is currently edited by Pradeep Chintagunta

More articles in Quantitative Marketing and Economics (QME) from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().