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| Mô hình Chuyển đổi Chế độ Markov (MS-AR / MS-VAR)× | 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≠ | 1989 | 2019 | 2005 |
| Người khởi xướng≠ | Hamilton (1989); Kim & Nelson (1999) | Wooldridge (textbook treatment); classical least squares | Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition |
| Loại≠ | Regime-switching time series model | Linear regression | Multivariate time-series model |
| Công trình gốc≠ | Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384. 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≠ | regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon |
| Liên quan≠ | 5 | 5 | 4 |
| Tóm tắt≠ | The Markov regime-switching model lets the parameters of a time series change probabilistically across hidden regimes governed by a Markov chain. Introduced by Hamilton (1989) and developed further by Kim and Nelson (1999), it automatically detects business-cycle phases such as expansions and contractions. | 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). |
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