Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель коррекции ошибок вектора (VECM)× | Модель ARIMA (авторегрессионная интегрированная скользящая средняя)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1987 | 1970 |
| Автор метода≠ | Robert F. Engle and Clive W. J. Granger | George Box and Gwilym Jenkins |
| Тип≠ | Multivariate time-series model | Time series forecasting model |
| Основополагающий источник≠ | Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. DOI ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Другие названия | VECM, error correction VAR, cointegrated VAR, vector equilibrium correction model | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Связанные≠ | 5 | 6 |
| Сводка≠ | The Vector Error Correction Model extends the Vector Autoregression (VAR) framework to a system of variables that share one or more long-run equilibrium relationships. It jointly models short-run dynamics and the speed at which each variable corrects back toward equilibrium after a shock, making it the standard tool for analysing cointegrated multivariate time series. | 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. |
| ScholarGateНабор данных ↗ |
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