ON THE ERROR STRUCTURE OF DISCRETE CHOICE MODELS.

The increasing popularity of the stated preference (SP) approach, together with the strong recommendation to practitioners that whenever possible SP data should be mixed with revealed preference (RP) data, has revived old doubts about the applicability of logit models in travel demand forecasting. We consider here the error structure of the most common practical cases and use Monte Carlo simulation to examine the performance of various functions including the powerful multinomial probit model. Our results confirm the traditional view that logit models are remarkably robust and should perform reasonably well in most practical cases.