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	Regresja kwantylowa na kwantylach (QQ)	Model NARDL (Nonlinear Autoregressive Distributed Lag)
Dziedzina	Ekonometria	Ekonometria
Rodzina	Regression model	Regression model
Rok powstania	2015	2014
Twórca	Sim and Zhou	Shin, Yu & Greenwood-Nimmo
Typ	Nonparametric quantile regression	Nonlinear cointegration model
Źródło pierwotne	Sim, N., & 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.
Inne nazwy	QQ regression, QQ approach, quantile-on-quantile approach, nonparametric quantile regression	NARDL, nonlinear bounds test, asymmetric ARDL, asymmetric cointegration model
Pokrewne	6	5
Podsumowanie	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.	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.