Methoden vergleichen

	Conditional Logit Model (McFadden)	Gemischtes Logit-Modell	Multinomiale Logistische Regression
Fachgebiet	Ökonometrie	Ökonometrie	Ökonometrie
Familie	Regression model	Regression model	Regression model
Entstehungsjahr	1974	2000	1974
Urheber	Daniel McFadden	Daniel McFadden & Kenneth Train	McFadden
Typ	Discrete choice model for alternative-specific covariates	Random-parameters discrete choice model	Multinomial logistic regression
Wegweisende Quelle	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	McFadden, D. (1974). Conditional Logit Analysis of Qualitative Choice Behavior. In P. Zarembka (Ed.), Frontiers in Econometrics (pp. 105-142). Academic Press. ISBN: 978-0127761503
Aliasnamen	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	multinomial logistic regression, polytomous logistic regression, softmax regression, Çok Kategorili Lojistik Regresyon
Verwandt	3	3	5
Zusammenfassung	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.	Multinomial logistic regression is a maximum-likelihood method for a nominal (unordered) dependent variable with more than two categories. Building on McFadden's 1974 treatment of qualitative choice, it gives each category its own set of coefficients relative to a reference category.