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A mathematical reformulation of the reference price

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  • Kevin Dayaratna
  • P. Kannan

Abstract

Reference prices have long been studied in applied economics and business research. One of the classic formulations of the reference price is in terms of an iterative function of past prices. There are a number of limitations of such a formulation, however. Such limitations include burdensome computational time to estimate parameters, an inability to truly account for customer heterogeneity, and an estimation procedure that implies a misspecified model. Managerial recommendations based on inferences from such a model can be quite misleading. We mathematically reformulate the reference price by developing a closed-form expansion that addresses the aforementioned issues, enabling one to elicit truly meaningful managerial advice from the model. We estimate our model on a real world data set to illustrate the efficacy of our approach. Our work is not only important from a modeling perspective, but also has valuable behavioral and managerial implications, which modelers and non-modelers alike should find useful. Copyright Springer Science+Business Media, LLC 2012

Suggested Citation

  • Kevin Dayaratna & P. Kannan, 2012. "A mathematical reformulation of the reference price," Marketing Letters, Springer, vol. 23(3), pages 839-849, September.
    • Handle: RePEc:kap:mktlet:v:23:y:2012:i:3:p:839-849
    DOI: 10.1007/s11002-012-9192-3
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    References listed on IDEAS

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    5. Kevin Dayaratna & Jesse Crosson & Chandler Hubbard, 2022. "Closed Form Bayesian Inferences for Binary Logistic Regression with Applications to American Voter Turnout," Stats, MDPI, vol. 5(4), pages 1-21, November.

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