Σύγκριση μεθόδων

	Δοκιμή Μη Γραμμικής Αιτιότητας Granger Hiemstra-Jones	Συγκλίνουσα Διασταυρούμενη Αντιστοίχιση (CCM)	Εντροπία Μεταφοράς
Πεδίο	Οικονομετρία	Αιτιακή Συμπερασματολογία	Αιτιακή Συμπερασματολογία
Οικογένεια	Hypothesis test	Machine learning	Machine learning
Έτος προέλευσης	1994	2012	2000
Δημιουργός	Craig Hiemstra & Jonathan Jones	George Sugihara et al.	Thomas Schreiber
Τύπος	Nonparametric hypothesis test	Nonlinear time-series causality test	Non-parametric information-theoretic measure
Θεμελιώδης πηγή	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.	Schreiber, T. (2000). Measuring information transfer. Physical Review Letters, 85(2), 461–464.
Εναλλακτικές ονομασίες	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	Schreiber Information Transfer, Directed Information Flow, Conditional Mutual Information (directed), Transfer Entropisi
Συναφείς	3	3	3
Σύνοψη	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.	Transfer Entropy (TE) is a non-parametric, information-theoretic measure of directed statistical dependence between two time series, introduced by Thomas Schreiber in 2000. Grounded in Shannon entropy, it quantifies how much information the past of one process Y reduces uncertainty about the next state of another process X, beyond what X's own past already provides. Unlike linear correlation or Granger causality, TE captures nonlinear interactions and requires no model assumptions about the underlying dynamics.