Salīdzināt metodes

	Nelineārās Grangera koincidences tests	Nelineārs vektora kļūdu labojuma modelis (Nelineārs VECM)
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1992-2006	1989–1998
Autors	Baek & Brock (1992); Hiemstra & Jones (1994); Diks & Panchenko (2006)	Granger & Lee (1989); Enders & Granger (1998)
Tips	Nonparametric causality test	Nonlinear time-series model
Pirmavots	Diks, C., & Panchenko, V. (2006). A new statistic and practical guidelines for nonparametric Granger causality testing. Journal of Economic Dynamics and Control, 30(9-10), 1647-1669.	Enders, W., & Granger, C. W. J. (1998). Unit-root tests and asymmetric adjustment with an example using the term structure of interest rates. Journal of Business & Economic Statistics, 16(3), 304–311.
Citi nosaukumi	nonlinear causality test, BDS-based causality, Diks-Panchenko test, nonparametric Granger causality	nonlinear VECM, NVECM, threshold VECM, asymmetric VECM
Saistītās	6	2
Kopsavilkums	Nonlinear Granger causality extends the classic linear Granger causality framework to detect predictive relationships that operate through nonlinear dynamics. Using nonparametric or semi-parametric statistics based on correlation integrals or kernel density estimation, it identifies whether past values of one variable improve forecasts of another beyond what any linear model can capture.	The Nonlinear VECM extends the standard linear VECM by allowing the speed of adjustment toward long-run equilibrium to differ depending on the sign, magnitude, or regime of deviations from that equilibrium. It captures asymmetric or threshold-driven dynamics in cointegrated time-series systems that a standard VECM would miss.