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Hiemstra-Jones icke-linjära Granger-kausalitetstest×Granger kausalitetstest×
ÄmnesområdeEkonometriEkonometri
FamiljHypothesis testRegression model
Ursprungsår19941969
UpphovspersonCraig Hiemstra & Jonathan JonesClive W. J. Granger
TypNonparametric hypothesis testTime-series predictive causality test
UrsprungskällaHiemstra, 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. DOI ↗Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438. DOI ↗
AliasHJ Nonlinear Causality Test, Hiemstra-Jones Test, Nonlinear Granger Causality (Hiemstra-Jones), HJ Nedensellik TestiGranger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik Testi
Närliggande35
SammanfattningThe 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.
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ScholarGateJämför metoder: Hiemstra-Jones Causality · Granger Causality. Hämtad 2026-06-18 från https://scholargate.app/sv/compare