Bandingkan metode
Tinjau metode pilihan Anda berdampingan; baris yang berbeda akan disorot.
| Model Ruang Keadaan (Kalman Filter)× | Model Peralihan Rezim Markov (MS-AR / MS-VAR)× | Model Deret Waktu Struktural (Model Struktural Dasar)× | |
|---|---|---|---|
| Bidang | Ekonometrika | Ekonometrika | Ekonometrika |
| Keluarga | Regression model | Regression model | Regression model |
| Tahun asal≠ | 1990 | 1989 | 1990 |
| Pencetus≠ | Harvey; Durbin & Koopman (state space treatment); Kalman filter | Hamilton (1989); Kim & Nelson (1999) | Andrew C. Harvey |
| Tipe≠ | State space time series model | Regime-switching time series model | State-space (unobserved components) time series model |
| Sumber perintis≠ | 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 ↗ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737 |
| Alias≠ | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) | regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR | BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM) |
| Terkait≠ | 4 | 5 | 4 |
| Ringkasan≠ | 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. | The Structural Time Series Model, in its Basic Structural Model (BSM) form, is Andrew Harvey's state-space approach that decomposes a series into separate stochastic trend, seasonal, cyclical, and irregular components. Developed in Harvey's 1990 treatment, it is prized for interpretability and component decomposition where ARIMA only delivers a black-box fit. |
| ScholarGateSet data ↗ |
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