Bandingkan metode
Tinjau metode pilihan Anda berdampingan; baris yang berbeda akan disorot.
| Model Ruang Keadaan (Kalman Filter)× | Autoregresi Vektor Bayesian (BVAR)× | Model Deret Waktu Struktural (Model Struktural Dasar)× | |
|---|---|---|---|
| Bidang | Ekonometrika | Ekonometrika | Ekonometrika |
| Keluarga | Regression model | Regression model | Regression model |
| Tahun asal≠ | 1990 | 1986 | 1990 |
| Pencetus≠ | Harvey; Durbin & Koopman (state space treatment); Kalman filter | Litterman (1986); Bańbura, Giannone & Reichlin (2010) | Andrew C. Harvey |
| Tipe≠ | State space time series model | Bayesian multivariate 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 ↗ | Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions—Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25-38. 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) | BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR) | 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. | Bayesian VAR adds Minnesota or other prior distributions to a vector autoregressive model to control over-parameterisation. Introduced by Litterman (1986) and extended to high dimensions by Bańbura, Giannone and Reichlin (2010), it outperforms classical VAR on short series and high-dimensional macroeconomic forecasts. | 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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