Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Modèle de sélection de Heckman (Heckit / Tobit Type II)× | Régression quantile× | |
|---|---|---|
| Domaine | Économétrie | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1979 | 1978 |
| Auteur d'origine≠ | James J. Heckman | Koenker & Bassett |
| Type≠ | Two-step sample selection model | Conditional quantile regression |
| Source fondatrice≠ | Heckman, J. J. (1979). Sample Selection Bias as a Specification Error. Econometrica, 47(1), 153–161. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Alias≠ | heckit, tobit type II, sample selection model, Heckman Seçim Modeli (Heckit / Tobit II) | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Apparentées≠ | 4 | 5 |
| Résumé≠ | The Heckman selection model, introduced by James J. Heckman in 1979, is a two-step model that corrects sample selection bias when the outcome is only observed for a non-random subset of cases. A probit selection equation models who is observed, and the outcome equation then corrects for the resulting bias using the inverse Mills ratio. | 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. |
| ScholarGateJeu de données ↗ |
|
|