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

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  • David Revelt and Kenneth Train.

Abstract

David Revelt and Kenneth Train. JEL#: C25, D12, L94. Keywords: energy suppliers, mixed logit, taste parameters 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. May 2000

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Bibliographic Info

Paper provided by University of California at Berkeley in its series Economics Working Papers with number E00-274.

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Date of creation: 01 May 2000
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Handle: RePEc:ucb:calbwp:e00-274

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  1. Hajivassiliou, Vassilis A & Ruud, Paul A., 1993. "Classical Estimation Methods for LDV Models Using Simulation," Department of Economics, Working Paper Series qt3cg196fr, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
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  4. 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.
  5. 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.
  6. Hamilton, James D., 1996. "Specification testing in Markov-switching time-series models," Journal of Econometrics, Elsevier, vol. 70(1), pages 127-157, January.
  7. 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.
  8. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
  9. Allenby, Greg M. & Rossi, Peter E., 1998. "Marketing models of consumer heterogeneity," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 57-78, November.
  10. Neeraj Arora & Greg M. Allenby & James L. Ginter, 1998. "A Hierarchical Bayes Model of Primary and Secondary Demand," Marketing Science, INFORMS, vol. 17(1), pages 29-44.
  11. Lee, L-F., 1990. "On Efficiency of Methods of Simulated Moments and Maximum Simulated Likelihood Estimation of Discrete Response Models," Papers 260, Minnesota - Center for Economic Research.
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  13. Griffiths, William E., 1971. "Estimation Of Actual Response Coefficients In The Hildreth-Houck Random Coefficient Model," Staff Papers 14275, University of Minnesota, Department of Applied Economics.
  14. 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.
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Cited by:
  1. Joseph Pancras, 2010. "A Framework to Determine the Value of Consumer Consideration Set Information for Firm Pricing Strategies," Computational Economics, Society for Computational Economics, vol. 35(3), pages 269-300, March.
  2. Gaudecker, H.M. von & Soest, A.H.O. van & Wengstrom, E., 2011. "Heterogeneity in risky choice behavior in a broad population," Open Access publications from Tilburg University urn:nbn:nl:ui:12-3857699, Tilburg University.
  3. 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).
  4. Hole, Arne Risa, 2008. "Modelling heterogeneity in patients' preferences for the attributes of a general practitioner appointment," Journal of Health Economics, Elsevier, vol. 27(4), pages 1078-1094, July.
  5. 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.
  6. Hans-Martin Gaudecker & Arthur Soest & Erik Wengström, 2012. "Experts in experiments," Journal of Risk and Uncertainty, Springer, vol. 45(2), pages 159-190, October.

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