There Is No Aggregate Bias: Why Macro Logit Models Work

Greg M Allenby and Peter Rossi ()

Journal of Business & Economic Statistics, 1991, vol. 9, issue 1, 1-14

Abstract: In this article, we examine the aggregation properties of (nested) logit models to understand their exceptional macro-level performance. The problem of aggregating micro logit models involves integrating nonlinear functions of model parameters over a distribution of consumer heterogeneity. The aggregation problem is analyzed using a mixture of analytic and simulation techniques, with the simulation experiments using actual panel data to calibrate the distribution of heterogeneity. We conclude that the practice of fitting aggregate logit models is theoretically justified under the following three conditions: (1) All consumers are exposed to the same marketing-mix variables, (2) the brands are close substitutes, and (3) the distribution of prices is not concentrated at an extreme value. These conditions are frequently met in store-level scanner data.

Date: 1991
References: Add references at CitEc
Citations: View citations in EconPapers (25)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:bes:jnlbes:v:9:y:1991:i:1:p:1-14

Ordering information: This journal article can be ordered from
http://www.amstat.org/publications/index.html

Access Statistics for this article

Journal of Business & Economic Statistics is currently edited by Jonathan H. Wright and Keisuke Hirano

More articles in Journal of Business & Economic Statistics from American Statistical Association
Bibliographic data for series maintained by Christopher F. Baum ().