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SUMMARY:Improving the efficiency of individualized designs for the mixed l
 ogit model by including covariates - Crabbe\, M (KU\, Leuven)
DTSTART:20110831T160000Z
DTEND:20110831T163000Z
UID:TALK32594@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Conjoint choice experiments have become an established tool to
  get a deeper insight in the choice behavior of consumers. Recently\, the 
 discrete choice literature focused attention on the use of covariates like
  demographics\, socio-economic variables or other individual-specific char
 acteristics in design and estimation of discrete choice models\, more spec
 ifically on whether the incorporation of such choice related respondent in
 formation aids in increasing estimation and prediction accuracy. The discr
 ete choice model considered in this paper is the panel mixed logit model. 
 This random-effects choice model accommodates preference heterogeneity and
  moreover\, accounts for the correlation between individuals successive ch
 oices. Efficient choice data for the panel mixed logit model is obtained b
 y individually adapted sequential Bayesian designs\, which are customized 
 to the specific preferences of a respondent\, and reliable estimates for t
 he model parameters are acquired by means of a hierarchical Bayes estimati
 on approach. This research extends both experimental design and model esti
 mation for the panel mixed logit model to include covariate information. S
 imulation studies of various experimental settings illustrate how the incl
 usion of influential covariates yields more accurate estimates for the ind
 ividual parameters in the panel mixed logit model. Moreover\, we show that
  the efficiency loss in design and estimation resulting from including cho
 ice unrelated respondent characteristics is negligible. \n
LOCATION:Seminar Room 1\, Newton Institute
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