Comparar métodos

	Regresión Cuantil-sobre-Cuantil (QQ)	Prueba de Causalidad de Granger
Campo	Econometría	Econometría
Familia	Regression model	Regression model
Año de origen	2015	1969
Autor original	Sim and Zhou	Clive W. J. Granger
Tipo	Nonparametric quantile regression	Causality test (F-test on VAR)
Fuente seminal	Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1-8.	Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424–438.
Alias	QQ regression, QQ approach, quantile-on-quantile approach, nonparametric quantile regression	Granger test, GC test, predictive causality test, Granger non-causality test
Relacionados	6	5
Resumen	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 Granger causality test is a statistical hypothesis test that determines whether past values of one time series help predict future values of another, beyond what that series' own past already explains. Introduced by Clive Granger in 1969, it is the standard approach for assessing predictive causality in VAR-based time-series analysis.