Estimating a semiparametric additive model for discrete choice data using backfitting algorithm - Doctolero, Patricia Gelin I.
Abstract (Discrete choice models are estimated with the assumption of existence of a link function. To relax this assumption, a semiparametric additive model of the utility function proposed. Quasi likelihood estimation is embedded into the backfitting algorithm and used in estimating the utility function. A simulation study if developed to evaluate the performance of the fitted model based on misclassification rate. The proposed model performed well in cases of linear and nonlinear nonparametric function given all alternatives have balanced data allocation. Moreover, the results showed that the model is robust to sample size lead to slight increase in the predictive ability. Using the simulated data, the scenarios cited performed better in terms of misclassification rate in validation than the generalized additive model for discrete choice data of Abe (1999).