Vertaile menetelmiä

	Johansenin kointegraatiotesti ja vektorikorjausmalli	ARIMA (Autoregressive Integrated Moving Average) -malli	Vektorien autoregressiomalli (VAR-malli)
Tieteenala	Rahoitus	Ekonometria	Ekonometria
Menetelmäperhe	Regression model	Regression model	Regression model
Syntyvuosi	1991	2015	2005
Kehittäjä	Søren Johansen	Box & Jenkins (Box-Jenkins methodology)	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Tyyppi	Multivariate cointegration / vector error correction model	Univariate time-series model	Multivariate time-series model
Alkuperäislähde	Johansen, S. (1991). Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica, 59(6), 1551-1580.	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	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Rinnakkaisnimet	Johansen test, VECM, vector error correction model, multivariate cointegration	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Liittyvät	3	5	4
Tiivistelmä	The Johansen procedure is a multivariate cointegration framework, introduced by Søren Johansen in 1991, that tests for long-run equilibrium relationships among several I(1) time series. It determines how many cointegrating vectors link the series and then builds a Vector Error Correction Model (VECM) to describe the short-run dynamics around that equilibrium.	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).	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).