The estimation of generalized extreme value models from choice-based samples

Abstract

In the presence of choice-based sampling strategies for data collection, the property of multinomial logit (MNL) models, that consistent estimates of all parameters but the constants can be obtained from an exogenous sample maximum likelihood (ESML) estimation, does not hold in general for generalized extreme value (GEV) models. We propose a consistent ESML estimator for GEV models in this context. We first identify a specific class of GEV models with the desired property that, similarly to MNL, the constants absorb the potential bias. We then propose a new and simple weighted conditional maximum likelihood (WCML) estimator for the more general case. Contrarily to the weighted exogenous sample maximum likelihood (WESML) estimator by Manski and Lerman [Manski, C., Lerman, S., 1977. The estimation of choice probabilities from choice-based samples. Econometrica 45, 1977–1988], the new WCML estimator does not require an external knowledge of the market shares. We show that this applies also to the case where alternatives are sampled from a large choice set, and we illustrate the use of the estimator on synthetic and real data.