Generalized Reverse Discrete Choice Models
Marketing practitioners and academics have shown a keen interest in the processes that drive consumers’ choices since the early work of Guadagni and Little (1982). Over the past decade or so, a number of alternative models have been proposed, implemented and analyzed. The common behavioral assumption that underlines these models of discrete choice is random utility maximization (RUM). The RUM assumption, in its simplest form, posits that a consumer with a finite set of brands to choose from chooses the brand that gives her the maximum amount of utility. An alternative approach would be to assume that consumers choose the alternative that offers them the least disutility. Our paper proposes and tests a broad class of generalized extreme value models based on this hypothesis. We model the decision process of the consumer the assumption random disutility minimization (RDM) and derive a new class of discrete choice models based on this assumption. Our findings reveal that there are significant theoretical and econometric differences between the discrete choice models derived from a RUM framework and the RDM framework proposed in this paper. On the theoretical front we find that the class of discrete choice models based on the assumption of disutility minimization is structurally different from the models in the literature. Further, the models in this class are available in closed form and exhibit the same flexibility as the GEV models proposed by McFadden (1978). In fact, the number of parameters are identical to and have the same interpretation as those obtained via RUM based GEV models. In addition to the theoretical differences we also uncover significant empirical insights. With the computing effort and time for both models being roughly the same this new set of models offers marketing academics and researchers a viable new tool with which to investigate discrete choice behavior. Copyright Springer Science + Business Media, Inc. 2005
Volume (Year): 3 (2005)
Issue (Month): 2 (June)
|Contact details of provider:|| Web page: http://www.springer.com|
|Order Information:||Web: http://www.springer.com/business+%26+management/marketing/journal/11129/PS2|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
- McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240.
- Peter M. Guadagni & John D. C. Little, 1983. "A Logit Model of Brand Choice Calibrated on Scanner Data," Marketing Science, INFORMS, vol. 2(3), pages 203-238.
- Jain, Dipak C & Vilcassim, Naufel J & Chintagunta, Pradeep K, 1994. "A Random-Coefficients Logit Brand-Choice Model Applied to Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 317-28, July.
- Anderson, Simon P. & de Palma, Andre, 1999.
"Reverse discrete choice models,"
Regional Science and Urban Economics,
Elsevier, vol. 29(6), pages 745-764, November.
- Bhat, Chandra R., 2003. "Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences," Transportation Research Part B: Methodological, Elsevier, vol. 37(9), pages 837-855, November.
- Winer, Russell S, 1986. " A Reference Price Model of Brand Choice for Frequently Purchased Products," Journal of Consumer Research, Oxford University Press, vol. 13(2), pages 250-56, September.
- Bhat, Chandra R., 2001. "Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 677-693, August.
- Dan Horsky & Paul Nelson, 1992. "New Brand Positioning and Pricing in an Oligopolistic Market," Marketing Science, INFORMS, vol. 11(2), pages 133-153.
- Joskow, Paul L & Mishkin, Frederic S, 1977.
"Electric Utility Fuel Choice Behavior in the United States,"
International Economic Review,
Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(3), pages 719-36, October.
- P. L. Joskow & F. S. Mishkin, 1974. "Electric Utility Fuel Choice Behavior in the United States," Working papers 143, Massachusetts Institute of Technology (MIT), Department of Economics.
- Swait, Joffre, 2003. "Flexible Covariance Structures for Categorical Dependent Variables through Finite Mixtures of Generalized Extreme Value Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 80-87, January.
- Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-90, July.
- Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-33, March.
- Füsun Gönül & Kannan Srinivasan, 1996. "Estimating the Impact of Consumer Expectations of Coupons on Purchase Behavior: A Dynamic Structural Model," Marketing Science, INFORMS, vol. 15(3), pages 262-279.
- Wen, Chieh-Hua & Koppelman, Frank S., 2001. "The generalized nested logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 627-641, August.
- K. Sudhir, 2001. "Competitive Pricing Behavior in the US Auto Market: A Structural Analysis," Yale School of Management Working Papers ysm228, Yale School of Management.
- P. K. Kannan & Gordon P. Wright, 1991. "Modeling and Testing Structured Markets: A Nested Logit Approach," Marketing Science, INFORMS, vol. 10(1), pages 58-82.
- Small, Kenneth A., 1994. "Approximate generalized extreme value models of discrete choice," Journal of Econometrics, Elsevier, vol. 62(2), pages 351-382, June.
- Jill E. Hobbs, 1997. "Measuring the Importance of Transaction Costs in Cattle Marketing," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(4), pages 1083-1095.
- Peter E. Rossi & Robert E. McCulloch & Greg M. Allenby, 1996. "The Value of Purchase History Data in Target Marketing," Marketing Science, INFORMS, vol. 15(4), pages 321-340.
- Vrinda Kadiyali & Pradeep Chintagunta & Naufel Vilcassim, 2000. "Manufacturer-Retailer Channel Interactions and Implications for Channel Power: An Empirical Investigation of Pricing in a Local Market," Marketing Science, INFORMS, vol. 19(2), pages 127-148, September.
- K. Sudhir, 2001. "Competitive Pricing Behavior in the Auto Market: A Structural Analysis," Marketing Science, INFORMS, vol. 20(1), pages 42-60, January.
- Teck-Hua Ho & Christopher S. Tang & David R. Bell, 1998. "Rational Shopping Behavior and the Option Value of Variable Pricing," Management Science, INFORMS, vol. 44(12-Part-2), pages S145-S160, December.
When requesting a correction, please mention this item's handle: RePEc:kap:qmktec:v:3:y:2005:i:2:p:175-200. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla)or (Rebekah McClure)
If references are entirely missing, you can add them using this form.