Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Régression Quantile-sur-Quantile sur Données de Panel× | Modèle à effets aléatoires sur données de panel× | |
|---|---|---|
| Domaine | Économétrie | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 2015 (QQ); panel applications from ~2018 | 1966 |
| Auteur d'origine≠ | Sim and Zhou (cross-section QQ); panel extension in applied energy/finance econometrics | Balestra & Nerlove |
| Type≠ | Nonparametric quantile regression | Panel data estimator |
| Source fondatrice≠ | Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1-8. DOI ↗ | Balestra, P., & Nerlove, M. (1966). Pooling cross section and time series data in the estimation of a dynamic model: The demand for natural gas. Econometrica, 34(3), 585–612. DOI ↗ |
| Alias | Panel QQ regression, panel QQ approach, panel quantile-on-quantile approach, PQQ regression | random effects estimator, RE model, GLS random effects, error components model |
| Apparentées≠ | 6 | 5 |
| Résumé≠ | Panel quantile-on-quantile (QQ) regression jointly maps any quantile of the outcome distribution onto any quantile of the predictor distribution across multiple cross-sectional units observed over time. It generalises Sim and Zhou's (2015) cross-sectional QQ framework to a panel setting, revealing a full dependence surface rather than a single average effect, while accounting for individual heterogeneity through fixed or random effects correction. | The panel random effects (RE) model treats individual-specific effects as random draws from a population distribution rather than fixed constants, enabling efficient estimation by generalised least squares and allowing inference about time-invariant regressors that are swept away in fixed effects estimation. |
| ScholarGateJeu de données ↗ |
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