Comparer des méthodes

	Régression quantile-sur-quantile avec rupture structurelle	Modèle ARDL non linéaire (NARDL)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2015-2020s	2014
Auteur d'origine	Extension combining Sim & Zhou (2015) QQR framework with Bai-Perron structural break methodology	Shin, Yu & Greenwood-Nimmo
Type	Nonparametric quantile regression with structural breaks	Nonlinear cointegration model
Source fondatrice	Sim, N., and Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1-8.	Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In R. C. Sickles & W. C. Horrace (Eds.), Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications (pp. 281–314). Springer.
Alias	SB-QQR, structural-break QQ regression, quantile-on-quantile with structural breaks, QQR with regime shifts	NARDL, nonlinear bounds test, asymmetric ARDL, asymmetric cointegration model
Apparentées	6	5
Résumé	Structural Break Quantile-on-Quantile Regression (SB-QQR) extends the quantile-on-quantile framework of Sim and Zhou (2015) by allowing regression slopes to differ across regimes separated by structural breaks. It maps how the effect of a predictor's quantile on an outcome's quantile changes not only across the full distributional space but also across distinct historical periods or policy regimes.	The Nonlinear ARDL (NARDL) model extends the linear ARDL bounds-testing framework to allow asymmetric long-run and short-run relationships. By decomposing the regressor into cumulative positive and negative partial sums, it tests whether increases and decreases in a variable exert different effects on the outcome — a feature especially relevant in financial and energy economics where positive and negative shocks rarely cancel out symmetrically.