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

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Author Info

  • 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)

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    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.

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    File URL: http://dx.doi.org/10.1287/mksc.1080.0464
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    Bibliographic Info

    Article provided by INFORMS in its journal Marketing Science.

    Volume (Year): 29 (2010)
    Issue (Month): 1 (01-02)
    Pages: 1-17

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

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    Related research

    Keywords: brand choice; choice models; decisions under uncertainty; decision making over time; econometric models; lab experiments; measurement and inference; probability models; simulation; stochastic models;

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    Cited by:
    1. Harmsen-van Hout, Marjolein J.W. & Dellaert, Benedict G.C. & Herings, P. Jean-Jacques, 2008. "Behavorial Effects in Individual Decisions of Network Formation," Research Memorandum 019, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    2. Cameron, Trudy Ann & DeShazo, J.R., 2013. "Demand for health risk reductions," Journal of Environmental Economics and Management, Elsevier, vol. 65(1), pages 87-109.
    3. Hoyos, David, 2010. "The state of the art of environmental valuation with discrete choice experiments," Ecological Economics, Elsevier, vol. 69(8), pages 1595-1603, June.
    4. N. Flynn, Terry & J. Peters, Tim & Coast, Joanna, 2013. "Quantifying response shift or adaptation effects in quality of life by synthesising best-worst scaling and discrete choice data," Journal of choice modelling, Elsevier, vol. 6(C), pages 34-43.
    5. Beck, Matthew J. & Rose, John M. & Hensher, David A., 2013. "Consistently inconsistent: The role of certainty, acceptability and scale in choice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 56(C), pages 81-93.
    6. Sclen, HÃ¥kon & Kallbekken, Steffen, 2011. "A choice experiment on fuel taxation and earmarking in Norway," Ecological Economics, Elsevier, vol. 70(11), pages 2181-2190, September.

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