Improving the efficiency of individualized designs for the mixed logit model by including covariates
Seminar Room 1, Newton Institute
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 characteristics in design and estimation of discrete choice models, more specifically on whether the incorporation of such choice related respondent information aids in increasing estimation and prediction accuracy. The discrete 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 choices. Efficient choice data for the panel mixed logit model is obtained by individually adapted sequential Bayesian designs, which are customized to the specific preferences of a respondent, and reliable estimates for the model parameters are acquired by means of a hierarchical Bayes estimation approach. This research extends both experimental design and model estimation for the panel mixed logit model to include covariate information. Simulation studies of various experimental settings illustrate how the inclusion of influential covariates yields more accurate estimates for the individual parameters in the panel mixed logit model. Moreover, we show that the efficiency loss in design and estimation resulting from including choice unrelated respondent characteristics is negligible.