เปรียบเทียบวิธี

	การทดสอบความเป็นเหตุเป็นผลแบบไม่เชิงเส้นของ Hiemstra-Jones	การทดสอบความเป็นเหตุเป็นผลแบบแกรนเจอร์ (Granger Causality Test)	เอนโทรปีการถ่ายทอด
สาขาวิชา	เศรษฐมิติ	เศรษฐมิติ	การอนุมานเชิงสาเหตุ
ตระกูล	Hypothesis test	Regression model	Machine learning
ปีกำเนิด	1994	1969	2000
ผู้ริเริ่ม	Craig Hiemstra & Jonathan Jones	Clive W. J. Granger	Thomas Schreiber
ประเภท	Nonparametric hypothesis test	Time-series predictive causality test	Non-parametric information-theoretic measure
แหล่งต้นตำรับ	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.	Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438.	Schreiber, T. (2000). Measuring information transfer. Physical Review Letters, 85(2), 461–464.
ชื่อเรียกอื่น	HJ Nonlinear Causality Test, Hiemstra-Jones Test, Nonlinear Granger Causality (Hiemstra-Jones), HJ Nedensellik Testi	Granger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik Testi	Schreiber Information Transfer, Directed Information Flow, Conditional Mutual Information (directed), Transfer Entropisi
ที่เกี่ยวข้อง	3	5	3
สรุป	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.	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.	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.