Σύγκριση μεθόδων

	Έλεγχος Μη Γραμμικής Αιτιότητας κατά Granger	Μη Γραμμικό Μοντέλο VAR
Πεδίο	Οικονομετρία	Οικονομετρία
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	1992-2006	1990s–2000s
Δημιουργός	Baek & Brock (1992); Hiemstra & Jones (1994); Diks & Panchenko (2006)	Tsay (1998); Krolzig (1997); Tong (1990) for threshold framework
Τύπος	Nonparametric causality test	Multivariate nonlinear time series model
Θεμελιώδης πηγή	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.	Tsay, R. S. (1998). Testing and modeling multivariate threshold models. Journal of the American Statistical Association, 93(443), 1188–1202.
Εναλλακτικές ονομασίες	nonlinear causality test, BDS-based causality, Diks-Panchenko test, nonparametric Granger causality	NLVAR, nonlinear vector autoregression, threshold VAR, TVAR
Συναφείς	6	4
Σύνοψη	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 VAR (NLVAR) model extends the standard vector autoregression by allowing the dynamic relationships among multiple time series to switch or change smoothly depending on an observed threshold variable, a latent regime state, or a smooth transition function. It is used when economic systems exhibit asymmetric responses, regime shifts, or state-dependent dynamics that a linear VAR cannot capture.