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| Modèle de régression probit× | Régression par Moindres Carrés Ordinaires (MCO)× | |
|---|---|---|
| Domaine | Économétrie | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 2018 | 2019 |
| Auteur d'origine≠ | Greene (textbook treatment); classical discrete-choice modelling | Wooldridge (textbook treatment); classical least squares |
| Type≠ | Binary discrete-choice model | Linear regression |
| Source fondatrice≠ | Greene, W. H. (2018). Econometric Analysis (8th ed.). Pearson. ISBN: 978-0134461366 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Alias≠ | probit regression, normit model, Probit Modeli | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Apparentées | 5 | 5 |
| Résumé≠ | 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). | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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