Comparar métodos

	Modelo Logit Condicional (McFadden)	Modelo Logit Mixto	Modelo de Elección Discreta Logit Anidado
Campo	Econometría	Econometría	Econometría
Familia	Regression model	Regression model	Regression model
Año de origen	1974	2000	1985
Autor original	Daniel McFadden	Daniel McFadden & Kenneth Train	Daniel McFadden; Ben-Akiva & Lerman
Tipo	Discrete choice model for alternative-specific covariates	Random-parameters discrete choice model	Discrete choice regression model
Fuente seminal	McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), Frontiers in Econometrics (pp. 105–142). Academic Press. ISBN: 978-0-12-776150-3	Train, K. E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press. ISBN: 978-0-521-74738-7	Ben-Akiva, M., & Lerman, S. R. (1985). Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press. ISBN: 978-0-262-02217-0
Alias	McFadden's Choice Model, Discrete Choice Logit, Alternative-Specific Logit, Koşullu Logit Modeli	Random Parameters Logit, Mixed Multinomial Logit, Error Components Logit, Karma Logit Modeli	Tree Logit Model, Hierarchical Logit Model, Generalized Extreme Value Logit, İç İçe Logit Modeli
Relacionados	3	3	3
Resumen	The Conditional Logit Model, introduced by Daniel McFadden in 1974, is a discrete-choice econometric model designed to explain an individual's selection among a finite set of mutually exclusive alternatives. Unlike multinomial logit, it uses covariates that vary across alternatives — such as price, travel time, or product attributes — making it ideally suited for revealed-preference studies in transportation, marketing, and labor economics.	The Mixed Logit model, introduced formally by McFadden and Train (2000) and elaborated in Train (2009), is a flexible discrete choice framework that allows preference parameters to vary randomly across decision-makers. By integrating standard logit probabilities over a mixing distribution of coefficients, it overcomes the restrictive independence of irrelevant alternatives (IIA) property and accommodates unobserved taste heterogeneity, panel data correlation, and complex substitution patterns across alternatives.	The Nested Logit model is a discrete choice framework that groups mutually exclusive alternatives into hierarchical nests, allowing correlated unobserved utilities within each nest while maintaining independence across nests. Introduced formally by Ben-Akiva and Lerman (1985) and grounded in McFadden's Generalized Extreme Value (GEV) theory, it extends the standard Multinomial Logit by relaxing the restrictive Independence of Irrelevant Alternatives assumption within predefined groups of similar alternatives.