So sánh phương pháp
Xem các phương pháp đã chọn cạnh nhau; những hàng khác biệt được làm nổi bật.
| Mô hình Hiệu chỉnh Sai số Vector (VECM)× | Hồi quy Bình phương Tối thiểu Thông thường (OLS)× | 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 | 2019 | 2005 |
| Người khởi xướng≠ | Engle & Granger | Wooldridge (textbook treatment); classical least squares | Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition |
| Loại≠ | Multivariate time-series model | Linear regression | 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 ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | 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) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | 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. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). | 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). |
| ScholarGateBộ dữ liệu ↗ |
|
|
|