 The estimation of generalized extreme value models from choice-based samplesM. Bierlaire, Denis Bolduc () and Daniel McFadden Transportation Research Part B: Methodological, 2008, vol. 42, issue 4, 381-394 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. Date: 2008 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:transb:v:42:y:2008:i:4:p:381-394 Ordering information: This journal article can be ordered from Access Statistics for this article Transportation Research Part B: Methodological is currently edited by Fred Mannering More articles in Transportation Research Part B: Methodological from Elsevier |
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