Sammenlign metoder

	Hiemstra-Jones' test for ikke-lineær Granger-kausalitet	Transfer Entropy
Fagfelt	Økonometri	Kausal inferens
Familie	Hypothesis test	Machine learning
Opprinnelsesår	1994	2000
Opphavsperson	Craig Hiemstra & Jonathan Jones	Thomas Schreiber
Type	Nonparametric hypothesis test	Non-parametric information-theoretic measure
Opprinnelig kilde	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.	Schreiber, T. (2000). Measuring information transfer. Physical Review Letters, 85(2), 461–464.
Alias	HJ Nonlinear Causality Test, Hiemstra-Jones Test, Nonlinear Granger Causality (Hiemstra-Jones), HJ Nedensellik Testi	Schreiber Information Transfer, Directed Information Flow, Conditional Mutual Information (directed), Transfer Entropisi
Relaterte	3	3
Sammendrag	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.	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.