Salīdzināt metodes

	Divēnu varbūtības modelis	Multinomiālā loģistiskā regresija	Probit regresijas modelis
Nozare	Ekonometrija	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model	Regression model
Izcelsmes gads	1970	1974	2018
Autors	J. R. Ashford & R. R. Sowden	McFadden	Greene (textbook treatment); classical discrete-choice modelling
Tips	Maximum-likelihood binary outcome model	Multinomial logistic regression	Binary discrete-choice model
Pirmavots	Ashford, J. R., & Sowden, R. R. (1970). Multi-variate probit analysis. Biometrics, 26(3), 535–546.	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	Greene, W. H. (2018). Econometric Analysis (8th ed.). Pearson. ISBN: 978-0134461366
Citi nosaukumi	Bivariate Binary Probit, Joint Probit Model, Two-Equation Probit, İki Değişkenli Probit	multinomial logistic regression, polytomous logistic regression, softmax regression, Çok Kategorili Lojistik Regresyon	probit regression, normit model, Probit Modeli
Saistītās	3	5	5
Kopsavilkums	The Bivariate Probit Model, introduced by Ashford and Sowden (1970), jointly estimates two binary outcome equations whose error terms are allowed to be correlated. By modeling both outcomes simultaneously under a bivariate normal distribution, it corrects for the dependence between decisions that separate probit regressions would ignore, producing consistent and efficient parameter estimates for researchers studying interrelated binary choices.	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.	The probit model is a regression method for a binary (0/1) outcome that maps a linear index of the predictors through the standard normal cumulative distribution function to produce a probability. It is a classical discrete-choice alternative to logistic regression, developed in standard econometrics treatments such as Greene's Econometric Analysis (2018).