Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Structural Time Series Model× | Modelo de cambio de régimen de Markov (MS-AR / MS-VAR)× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1990 | 1989 |
| Autor original≠ | Andrew C. Harvey | Hamilton (1989); Kim & Nelson (1999) |
| Tipo≠ | State-space (unobserved components) time series model | Regime-switching time series model |
| Fuente seminal≠ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737 | 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 ↗ |
| Alias≠ | BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM) | regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR |
| Relacionados≠ | 4 | 5 |
| Resumen≠ | 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. | 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. |
| ScholarGateConjunto de datos ↗ |
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