Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Модель простір-стан (фільтр Калмана)× | Модель ARIMA (Авторегресійна інтегрована ковзна середня)× | Модель Марковського перемикання режимів (MS-AR / MS-VAR)× | |
|---|---|---|---|
| Галузь | Економетрика | Економетрика | Економетрика |
| Родина | Regression model | Regression model | Regression model |
| Рік появи≠ | 1990 | 2015 | 1989 |
| Автор методу≠ | Harvey; Durbin & Koopman (state space treatment); Kalman filter | Box & Jenkins (Box-Jenkins methodology) | Hamilton (1989); Kim & Nelson (1999) |
| Тип≠ | State space time series model | Univariate time-series model | Regime-switching time series model |
| Основоположне джерело≠ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. 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 | 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 ↗ |
| Інші назви≠ | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR |
| Пов'язані≠ | 4 | 5 | 5 |
| Підсумок≠ | A state space model is a general time series framework that describes a series through unobserved (latent) state variables linked by a measurement equation and a transition equation, with the states estimated in real time by the Kalman filter. Developed in the state space tradition of Harvey (1990) and Durbin & Koopman (2012), it nests ARIMA and exponential smoothing as special cases. | 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 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. |
| ScholarGateНабір даних ↗ |
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