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Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Модел в състояние пространство (Калманов филтър)× | Модел на Марковски превключващи се режими (MS-AR / MS-VAR)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1990 | 1989 |
| Създател≠ | Harvey; Durbin & Koopman (state space treatment); Kalman filter | Hamilton (1989); Kim & Nelson (1999) |
| Тип≠ | State space time series model | Regime-switching time series model |
| Основополагащ източник≠ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ | 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 ↗ |
| Други названия≠ | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) | regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR |
| Свързани≠ | 4 | 5 |
| Резюме≠ | 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. | 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. |
| ScholarGateНабор от данни ↗ |
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