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

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.

Modeling Methods for Discrete Choice Analysis

This paper introduces new forms, sampling and estimation approaches fordiscrete choice models. The new models include behavioral specifications oflatent class choice models, multinomial probit, hybrid logit, andnon-parametric methods. Recent contributions also include new specializedchoice based sample designs that permit greater efficiency in datacollection. Finally, the paper describes recent developments in the use ofsimulation methods for model estimation. These developments are designed toallow the applications of discrete choice models to a wider variety ofdiscrete choice problems.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47225/1/11002_2004_Article_138116.pd

Mixed Logit With Repeated Choices: Households' Choices Of Appliance Efficiency Level

Mixed logit models, also called random-parameters or errorcomponents logit, are a generalization of standard logit that do not exhibit the restrictive "independence from irrelevant alternatives" property and explicitly account for correlations in unobserved utility over repeated choices by each customer. Mixed logits are estimated for households' choices of appliances under utility-sponsored programs that offer rebates or loans on high-efficiency appliances. © 1998 by the President and Fellows of Harvard College and the Massachusetts Institute of Technolog

Households ’ Choices of Appliance Efficiency Level

Abstract: Mixed logit models, also called random-parameters or error-components logit, are a generalization of standard logit that do not exhibit the restrictive "independence from irrelevant alternatives " property and explicitly account for correlations in unobserved utility over repeated choices by each customer. Mixed logits are estimated for households ' choices of appliances under utility-sponsored programs that offer rebates or loans on high-efficiency appliances

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

energy suppliers, mixed logit, taste parameters, Business, Economics, Energy Policy, Infrastructure, Science and Technology Policy

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

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