Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Модель пробіт-регресії× | Квантильна регресія× | |
|---|---|---|
| Галузь | Економетрика | Економетрика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 2018 | 1978 |
| Автор методу≠ | Greene (textbook treatment); classical discrete-choice modelling | Koenker & Bassett |
| Тип≠ | Binary discrete-choice model | Conditional quantile regression |
| Основоположне джерело≠ | Greene, W. H. (2018). Econometric Analysis (8th ed.). Pearson. ISBN: 978-0134461366 | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Інші назви | probit regression, normit model, Probit Modeli | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Пов'язані | 5 | 5 |
| Підсумок≠ | 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). | Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails. |
| ScholarGateНабір даних ↗ |
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