Сравнение методов

	Модель ARIMA (авторегрессионная интегрированная скользящая средняя)	Модель GARCH (прогнозирование волатильности)	Модель векторной авторегрессии (VAR)
Область	Эконометрика	Эконометрика	Эконометрика
Семейство	Regression model	Regression model	Regression model
Год появления	1970	1986	2005
Автор метода	George Box and Gwilym Jenkins	Tim Bollerslev	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Тип	Time series forecasting model	Conditional volatility model	Multivariate time-series model
Основополагающий источник	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Другие названия	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Связанные	6	5	4
Сводка	The ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics.	The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series.	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).