השוואת שיטות

	מודל רגרסיה אוטורגרסיבי מולג עם השהיות מפוזרות לא ליניארי (NARDL)	מבחן סיבתיות גריינג'ר	מודל אוטורגרסיה וקטורית (VAR)
תחום	אקונומטריקה	אקונומטריקה	אקונומטריקה
משפחה	Regression model	Regression model	Regression model
שנת המקור	2014	1969	2005
הוגה השיטה	Shin, Yu, and Greenwood-Nimmo	Clive W. J. Granger	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
סוג	Nonlinear cointegration model	Time-series predictive causality test	Multivariate time-series model
מקור מכונן	Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In R. C. Sickles & W. C. Horrace (Eds.), Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications (pp. 281-314). Springer.	Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438.	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
כינויים	NARDL, nonlinear ARDL, asymmetric ARDL, nonlinear bounds test	Granger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik Testi	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
קשורות	4	5	4
תקציר	The Nonlinear ARDL (NARDL) model extends the linear ARDL bounds-testing framework to allow asymmetric long-run and short-run relationships. By decomposing an explanatory variable into its positive and negative partial sums, it tests whether increases and decreases in a regressor have different effects on the dependent variable — a feature that linear cointegration methods 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.	Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005).