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| Двумерен пробит модел× | Мултиномиална логистична регресия× | Пробит регресионен модел× | |
|---|---|---|---|
| Област | Иконометрия | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model | Regression model |
| Година на възникване≠ | 1970 | 1974 | 2018 |
| Създател≠ | J. R. Ashford & R. R. Sowden | McFadden | Greene (textbook treatment); classical discrete-choice modelling |
| Тип≠ | Maximum-likelihood binary outcome model | Multinomial logistic regression | Binary discrete-choice model |
| Основополагащ източник≠ | Ashford, J. R., & Sowden, R. R. (1970). Multi-variate probit analysis. Biometrics, 26(3), 535–546. DOI ↗ | 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 |
| Други названия≠ | 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 |
| Свързани≠ | 3 | 5 | 5 |
| Резюме≠ | 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). |
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