Vertaile menetelmiä

	Vektorien autoregressiomalli (VAR-malli)	ARIMA (Autoregressive Integrated Moving Average) -malli	OLS-regressio (Ordinary Least Squares)
Tieteenala	Ekonometria	Ekonometria	Ekonometria
Menetelmäperhe	Regression model	Regression model	Regression model
Syntyvuosi	2005	2015	2019
Kehittäjä	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition	Box & Jenkins (Box-Jenkins methodology)	Wooldridge (textbook treatment); classical least squares
Tyyppi	Multivariate time-series model	Univariate time-series model	Linear regression
Alkuperäislähde	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.	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	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Rinnakkaisnimet	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Liittyvät	4	5	5
Tiivistelmä	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).	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).	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).