On negative correlation: a comparison between Multinomial Probit and GEV-based discrete choice models

General extreme value (GEV)-type models such as Nested Logit (NL) and Cross-Nested Logit (CNL) have gained popularity for their closed-form formulation of the choice probabilities. A key assumption in GEV estimation process is that any correlation between the error terms is necessarily non-negative. No fundamental reason indicates that negative correlations should not occur from a behavioral perspective in the real world. In this paper, we investigate models’ outcomes when alternatives exhibit negative correlation. In experiments using synthetic databases, we estimate and validate Multinomial Probit (MNP) models that correctly handle negative correlations and we compare coefficients’ estimates and correlations to those obtained with GEV models. A real case study in which choices reveal the presence of negative correlations is also used to assess the performances of the proposed models. Results are obtained with NL, CNL and Mixed Logit models and compared to MNP. The implications for further practices are discussed.