Comparer des méthodes

	Régression Quantile-sur-Quantile sur Données de Panel	Test de Causalité de Granger sur Données de Panel
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2015 (QQ); panel applications from ~2018	1988–2012
Auteur d'origine	Sim and Zhou (cross-section QQ); panel extension in applied energy/finance econometrics	Holtz-Eakin, Newey & Rosen (1988); Dumitrescu & Hurlin (2012)
Type	Nonparametric quantile regression	Causality test
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.	Dumitrescu, E.-I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling, 29(4), 1450–1460.
Alias	Panel QQ regression, panel QQ approach, panel quantile-on-quantile approach, PQQ regression	panel causality test, Dumitrescu-Hurlin test, heterogeneous panel causality, panel Granger test
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 Granger Causality test examines whether past values of one variable help predict another variable across multiple cross-sectional units observed over time. It extends the classical Granger causality framework to panel data, accounting for cross-sectional heterogeneity and enabling more powerful inference by pooling information across units.