Krahasoni metodat

	Model ARIMA (Autoregressive Integrated Moving Average)	Granger Causality	Modeli i Autoregresionit Vektorial (VAR)
Fusha	Ekonometri	Ekonometri	Ekonometri
Familja	Regression model	Regression model	Regression model
Viti i origjinës	2015	1969	2005
Krijuesi	Box & Jenkins (Box-Jenkins methodology)	Clive W. J. Granger	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Lloji	Univariate time-series model	Time-series predictive causality test	Multivariate time-series model
Burimi themelues	Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021	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.
Emërtime të tjera	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	Granger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik Testi	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Të lidhura	5	5	4
Përmbledhja	ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).	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).