השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| מודל סדרות עתיות מבני (מודל מבני בסיסי)× | מודל מיתוג-משטרים של מרקוב (MS-AR / MS-VAR)× | |
|---|---|---|
| תחום | אקונומטריקה | אקונומטריקה |
| משפחה | Regression model | Regression model |
| שנת המקור≠ | 1990 | 1989 |
| הוגה השיטה≠ | Andrew C. Harvey | Hamilton (1989); Kim & Nelson (1999) |
| סוג≠ | State-space (unobserved components) time series model | Regime-switching time series model |
| מקור מכונן≠ | 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 ↗ |
| כינויים≠ | BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM) | regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR |
| קשורות≠ | 4 | 5 |
| תקציר≠ | 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. |
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