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Customer-Specific Taste Parameters and Mixed Logit: Households' Choice of Electricity Supplier

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Author Info
David Revelt (University of California, Berkeley)
Kenneth Train (University of California, Berkeley)

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Abstract

In a discrete choice situation, information about the tastes of each sampled customer is inferred from estimates of the distribution of tastes in the population. First, maximum likelihood procedures are used to estimate the distribution of tastes in the population using the pooled data for all sampled customers. Then, the distribution of tastes of each sampled customer is derived conditional on the observed data for that customer and the estimated population distribution of tastes (accounting for uncertainty in the population estimates.) We apply the method to data on residential customers' choice among energy suppliers in conjoint-type experiments. The estimated distribution of tastes provides practical information that is useful for suppliers in designing their offers. The conditioning for individual customers is found to differentiate customers effectively for marketing purposes and to improve considerably the predictions in new situations.

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Paper provided by EconWPA in its series Econometrics with number 0012001.

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Length: 31 pages
Date of creation: 02 Jan 2001
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Handle: RePEc:wpa:wuwpem:0012001

Note: 31 pages Acrobat .pdf
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Web page: http://129.3.20.41

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Find related papers by JEL classification:
C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models
D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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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.:
  1. Kenneth Train, 2000. "Halton Sequences for Mixed Logit," Department of Economics, Working Paper Series 1035, Department of Economics, Institute for Business and Economic Research, UC Berkeley. [Downloadable!]
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  2. Algers, Staffan & Bergström, Pål & Dahlberg, Matz & Lindqvist Dillén, Johanna, 1998. "Mixed Logit Estimation of the Value of Travel Time," Working Paper Series 1998:15, Uppsala University, Department of Economics. [Downloadable!]
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  3. David Revelt & Kenneth Train, 1998. "Mixed Logit With Repeated Choices: Households' Choices Of Appliance Efficiency Level," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 647-657, November. [Downloadable!] (restricted)
  4. Griffiths, W.E., 1971. "Estimation Of Actual Response Coefficients In The Hildreth-Houck Random Coefficient Model," Staff Papers 14275, University of Minnesota, Department of Applied Economics. [Downloadable!]
  5. Bhat, Chandra R., 1998. "Accommodating variations in responsiveness to level-of-service measures in travel mode choice modeling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 32(7), pages 495-507, September. [Downloadable!] (restricted)
  6. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470. [Downloadable!]
  7. 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. [Downloadable!] (restricted)
  8. Allenby, Greg M. & Rossi, Peter E., 1998. "Marketing models of consumer heterogeneity," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 57-78, November. [Downloadable!] (restricted)
  9. BOLDUC, Denis & FORTIN, Bernard & GORDON, Stephen, 1995. "Multinomial Probit Estimation of Spatially Interdependent Choices: an Empirical Comparison of Two New Techniques," Cahiers de recherche 9508, Université Laval - Département d'économique.
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  10. Hamilton, James D., 1996. "Specification testing in Markov-switching time-series models," Journal of Econometrics, Elsevier, vol. 70(1), pages 127-157, January. [Downloadable!] (restricted)
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  11. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333. [Downloadable!] (restricted)
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Cited by:
(explanations, 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.)

  1. Stephane Hess & John W. Polak, 2004. "An analysis of parking behaviour using discrete choice models calibrated on SP datasets," ERSA conference papers ersa04p60, European Regional Science Association. [Downloadable!]
  2. Francisco J. Más & Juan Luis Nicolau, 2006. "A New Marketing Segmentation Approach Based On Marginal Individual Utilities: Applying Crm Is Not A Chimera Anymore," Working Papers. Serie EC 2006-16, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie). [Downloadable!]
  3. Arne Risa Hole, 2007. "Modelling Heterogeneity in Patients' Preferences for the Attributes of a General Practitioner Appointment," Working Papers 022cherp, Centre for Health Economics, University of York. [Downloadable!]
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