Alleviating the Constant Stochastic Variance Assumption in Decision Research: Theory, Measurement, and Experimental Test

Linda Court Salisbury () and Fred M. Feinberg ()
Additional contact information
Linda Court Salisbury: Carroll School of Management, Boston College, Chestnut Hill, Massachusetts 02467
Fred M. Feinberg: Stephen M. Ross School of Business, University of Michigan, Ann Arbor, Michigan 48109

Marketing Science, 2010, vol. 29, issue 1, 1-17

Abstract: Analysts often rely on methods that presume constant stochastic variance, even though its degree can differ markedly across experimental and field settings. This reliance can lead to misestimation of effect sizes or unjustified theoretical or behavioral inferences. Classic utility-based discrete-choice theory makes sharp, testable predictions about how observed choice patterns should change when stochastic variance differs across items, brands, or conditions. We derive and examine the implications of assuming constant stochastic variance for choices made under different conditions or at different times, in particular, whether substantive effects can arise purely as artifacts. These implications are tested via an experiment designed to isolate the effects of stochastic variation in choice behavior. Results strongly suggest that the stochastic component should be carefully modeled to differ across both available brands and temporal conditions, and that its variance may be relatively greater for choices made for the future. The experimental design controls for several alternative mechanisms (e.g., flexibility seeking), and a series of related models suggest that several econometrically detectable explanations like correlated error, state dependence, and variety seeking add no explanatory power. A series of simulations argues for appropriate flexibility in discrete-choice specification when attempting to detect temporal stochastic inflation effects.

Keywords: brand choice; choice models; decisions under uncertainty; decision making over time; econometric models; lab experiments; measurement and inference; probability models; simulation; stochastic models (search for similar items in EconPapers)
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (27)

Downloads: (external link)
http://dx.doi.org/10.1287/mksc.1080.0464 (application/pdf)

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:inm:ormksc:v:29:y:2010:i:1:p:1-17

Access Statistics for this article

More articles in Marketing Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().