Methoden vergelijken

	Quantile-on-Quantile (QQ) Regressie	Niet-lineair ARDL (NARDL) Model
Vakgebied	Econometrie	Econometrie
Familie	Regression model	Regression model
Jaar van ontstaan	2015	2014
Grondlegger	Sim and Zhou	Shin, Yu & Greenwood-Nimmo
Type	Nonparametric quantile regression	Nonlinear cointegration model
Oorspronkelijke bron	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.
Aliassen	QQ regression, QQ approach, quantile-on-quantile approach, nonparametric quantile regression	NARDL, nonlinear bounds test, asymmetric ARDL, asymmetric cointegration model
Verwant	6	5
Samenvatting	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.