Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель ARIMA (авторегрессионная интегрированная скользящая средняя)× | Модель DCC-GARCH (динамическая условная корреляция)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1970 | 2002 |
| Автор метода≠ | George Box and Gwilym Jenkins | Robert F. Engle |
| Тип≠ | Time series forecasting model | Multivariate volatility model |
| Основополагающий источник≠ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ | Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350. DOI ↗ |
| Другие названия | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) | DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC |
| Связанные≠ | 6 | 5 |
| 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 DCC-GARCH model, introduced by Engle (2002), extends univariate GARCH to capture time-varying correlations between multiple financial time series. It decomposes the multivariate conditional covariance matrix into individual volatility processes and a dynamic correlation matrix, allowing correlations to fluctuate over time while remaining computationally tractable even with many series. |
| ScholarGateНабор данных ↗ |
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