This study is aimed to develop a dynamic discrete choice model with latent market segments using panel data. The model system consists of two models: a latent segmentation model and a choice model with serial correlation. This model system is applied to panel data of shopping place choice. The estimation results shows that the latent segmentation model significantly improved goodness of fit and the choice model yielded more efficient parameter estimates by taking into account of serial correlation.