Сравнение на методи

	Тест за нелинейна причинност по напречното корелиране на Хиемстра-Джоунс	Конвергентно кръстосано картографиране (CCM)	Тест за причинност на Грейнджър
Област	Иконометрия	Причинно-следствено заключение	Иконометрия
Семейство	Hypothesis test	Machine learning	Regression model
Година на възникване	1994	2012	1969
Създател	Craig Hiemstra & Jonathan Jones	George Sugihara et al.	Clive W. J. Granger
Тип	Nonparametric hypothesis test	Nonlinear time-series causality test	Time-series predictive causality test
Основополагащ източник	Hiemstra, C., & Jones, J. D. (1994). Testing for linear and nonlinear Granger causality in the stock price-volume relation. The Journal of Finance, 49(5), 1639–1664.	Sugihara, G., et al. (2012). Detecting causality in complex ecosystems. Science, 338(6106), 496–500.	Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438.
Други названия	HJ Nonlinear Causality Test, Hiemstra-Jones Test, Nonlinear Granger Causality (Hiemstra-Jones), HJ Nedensellik Testi	CCM, Cross-Convergent Mapping, Empirical Dynamic Modelling Causality, Yakınsak Çapraz Haritalama	Granger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik Testi
Свързани	3	3	5
Резюме	The Hiemstra-Jones test, introduced in 1994, is a nonparametric procedure for detecting nonlinear causal relationships between two time series after removing their linear interdependencies. Developed in the context of stock price and trading volume dynamics, it extends the standard linear Granger causality framework by using correlation integral statistics to detect predictability arising from nonlinear mechanisms that linear VAR models cannot capture.	Convergent Cross Mapping (CCM) is a nonlinear, state-space method for detecting causality between time-series variables embedded in a shared dynamical system. Introduced by George Sugihara and colleagues in their landmark 2012 Science paper, CCM exploits Takens' embedding theorem: if variable X causally influences Y, the historical record of Y contains enough information to recover the states of X. Causality is confirmed when cross-map skill improves—converges—as the time-series library grows longer.	The Granger causality test, introduced by Clive W. J. Granger in 1969, assesses whether the past values of one time series help predict another beyond what the latter's own past already explains. It defines causality in a strictly predictive sense rather than as a structural or physical cause.