Võrdle meetodeid

	ARIMA (autoregressiivne integreeritud liikuv keskmine) mudel	Dünaamiline faktormudel	Vektorautoregressiooni (VAR) mudel
Valdkond	Ökonomeetria	Ökonomeetria	Ökonomeetria
Perekond	Regression model	Regression model	Regression model
Tekkeaasta	2015	2002	2005
Looja	Box & Jenkins (Box-Jenkins methodology)	James Stock & Mark Watson	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Tüüp	Univariate time-series model	Latent-factor time-series model	Multivariate time-series model
Algallikas	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	Stock, J. H., & Watson, M. W. (2002). Macroeconomic forecasting using diffusion indexes. Journal of Business & Economic Statistics, 20(2), 147–162.	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Rööpnimetused	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	Diffusion Index Model, Large-Scale Factor Model, Approximate Factor Model, Dinamik Faktör Modeli	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Seotud	5	2	4
Kokkuvõte	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).	A Dynamic Factor Model (DFM) extracts a small number of latent common factors from a large panel of economic time series and uses those factors to forecast or nowcast a target variable. Formalized for macroeconomic forecasting by James Stock and Mark Watson in their 2002 Journal of Business & Economic Statistics paper, DFMs handle hundreds of indicators simultaneously while avoiding the curse of dimensionality that plagues traditional multivariate models.	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).