From static towards dynamic discrete choice modelling

The main aim of the present paper is to survey some major trends in current research in the field of discrete choice modelling, with particular emphasis on dynamic approaches. The paper is organized as follows. Section 2 provides a brief overview of static disaggregate choice modelling and random utility maximization, based inter alia on multinomial logit and/or probit models, generalized extreme value models and nested logit models. Particular attention is given here to model representation issues, sampling and estimation issues and model performance issues. Next, section 3 is devoted to some recent developments in the rapidly growing new field of dynamic discrete choice modelling. In contrast to stochastic panel data models of buying behaviour, dynamic discrete choice models incorporate explanatory variables and take adaptive behaviour explicitly into account (i.e., the effect of past experience on choice behaviour). Several dynamic discrete choice model approaches are summarized. Special attention is paid to the seminal work of Heckman. In the final section, complementary and alternative approaches to dynamic choice modelling are discussed, such as the human activity constraint approach, the computational process modelling approach and the master equation approach. It is concluded that contextual effects, multi-actor or synergetic interactions and shifting individual preferences based on learning principles are of primary importance in dynamic discrete choice modelling.