Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Модел на Марковски превключващи се режими (MS-AR / MS-VAR)× | Структурен модел на времеви редове (Основен структурен модел)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1989 | 1990 |
| Създател≠ | Hamilton (1989); Kim & Nelson (1999) | Andrew C. Harvey |
| Тип≠ | Regime-switching time series model | State-space (unobserved components) time series model |
| Основополагащ източник≠ | 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 |
| Други названия≠ | regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR | BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM) |
| Свързани≠ | 5 | 4 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
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