Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель ARMA (авторегрессионная скользящая средняя)× | Векторная авторегрессия (VAR)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1970 | 1980 |
| Автор метода≠ | George E. P. Box and Gwilym M. Jenkins | Christopher A. Sims |
| Тип≠ | Time series model | Multivariate time-series model |
| Основополагающий источник≠ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ | Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48. DOI ↗ |
| Другие названия | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) | VAR, VAR model, vector autoregressive model, multivariate autoregression |
| Связанные | 5 | 5 |
| Сводка≠ | The ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a moving average part that accounts for past q error terms. It is the foundational framework of the Box-Jenkins methodology for univariate time series modelling and short-run forecasting. | Vector Autoregression is a multivariate time-series model in which each variable is regressed on its own lags and the lags of all other variables in the system. Originally proposed by Sims (1980) as a data-driven alternative to large structural macroeconomic models, VAR has become the standard workhorse for dynamic analysis in empirical economics and finance. |
| ScholarGateНабор данных ↗ |
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