Uporedite metode

	Test nelinearne Granger kauzalnosti	Nelinearni VAR model
Oblast	Ekonometrija	Ekonometrija
Porodica	Regression model	Regression model
Godina nastanka	1992-2006	1990s–2000s
Tvorac	Baek & Brock (1992); Hiemstra & Jones (1994); Diks & Panchenko (2006)	Tsay (1998); Krolzig (1997); Tong (1990) for threshold framework
Tip	Nonparametric causality test	Multivariate nonlinear time series model
Temeljni izvor	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.
Drugi nazivi	nonlinear causality test, BDS-based causality, Diks-Panchenko test, nonparametric Granger causality	NLVAR, nonlinear vector autoregression, threshold VAR, TVAR
Srodne	6	4
Sažetak	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.