Comparer des méthodes

	Régression Quantile-sur-Quantile de Fourier	Régression quantile-quantile (QQ)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2015-2020s	2015
Auteur d'origine	Extension combining Sim & Zhou (2015) QQ regression with Fourier flexible-form smoothing	Sim and Zhou
Type	Nonparametric quantile regression with Fourier smoothing	Nonparametric quantile regression
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.	Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1-8.
Alias	Fourier QQ regression, Fourier-QQR, Fourier quantile regression with quantile regressors, smooth structural-break QQ regression	QQ regression, QQ approach, quantile-on-quantile approach, nonparametric quantile regression
Apparentées	6	6
Résumé	Fourier quantile-on-quantile regression extends the quantile-on-quantile (QQ) framework of Sim and Zhou (2015) by embedding Fourier trigonometric terms into the local linear quantile model. This allows the estimated dependence between the quantiles of one variable and the quantiles of another to vary smoothly over time, capturing gradual structural change without imposing a known break date.	Quantile-on-quantile regression is a nonparametric technique that estimates how the quantiles of one variable depend on the quantiles of another. By combining standard quantile regression with local linear smoothing, it produces a full two-dimensional surface of slope coefficients indexed by both the quantile of the outcome and the quantile of the predictor, revealing heterogeneous and asymmetric dependency structures invisible to standard regression.