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| Mô hình ARIMA (Autoregressive Integrated Moving Average)× | Kiểm định nhân quả Granger× | Mô hình Tự hồi quy Vector (VAR)× | |
|---|---|---|---|
| Lĩnh vực | Kinh tế lượng | Kinh tế lượng | Kinh tế lượng |
| Họ | Regression model | Regression model | Regression model |
| Năm ra đời≠ | 2015 | 1969 | 2005 |
| Người khởi xướng≠ | Box & Jenkins (Box-Jenkins methodology) | Clive W. J. Granger | Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition |
| Loại≠ | Univariate time-series model | Time-series predictive causality test | Multivariate time-series model |
| Công trình gốc≠ | 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 | Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438. DOI ↗ | Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. DOI ↗ |
| Tên gọi khác≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | Granger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik Testi | vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon |
| Liên quan≠ | 5 | 5 | 4 |
| Tóm tắt≠ | 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). | The Granger causality test, introduced by Clive W. J. Granger in 1969, assesses whether the past values of one time series help predict another beyond what the latter's own past already explains. It defines causality in a strictly predictive sense rather than as a structural or physical cause. | 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). |
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