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| Mô hình Hiệu chỉnh Sai số Vector (VECM)× | Mô hình ARIMA (Autoregressive Integrated Moving Average)× | 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≠ | 1987 | 2015 | 2005 |
| Người khởi xướng≠ | Engle & Granger | Box & Jenkins (Box-Jenkins methodology) | Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition |
| Loại≠ | Multivariate time-series model | Univariate time-series model | Multivariate time-series model |
| Công trình gốc≠ | 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., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 | Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. DOI ↗ |
| Tên gọi khác≠ | vector error correction model, error correction model, cointegration model, VECM (Vektör Hata Düzeltme Modeli) | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon |
| Liên quan≠ | 4 | 5 | 4 |
| Tóm tắt≠ | The Vector Error Correction Model is a multivariate time-series model for cointegrated series that captures both their short-run dynamics and their long-run equilibrium relationship. It was introduced by Engle and Granger in 1987 as part of the cointegration and error-correction framework. | 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). | 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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