השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| מודל ARIMA (Autoregressive Integrated Moving Average)× | מודל מיתוג-משטרים של מרקוב (MS-AR / MS-VAR)× | מודל סדרות עתיות מבני (מודל מבני בסיסי)× | |
|---|---|---|---|
| תחום | אקונומטריקה | אקונומטריקה | אקונומטריקה |
| משפחה | Regression model | Regression model | Regression model |
| שנת המקור≠ | 2015 | 1989 | 1990 |
| הוגה השיטה≠ | Box & Jenkins (Box-Jenkins methodology) | Hamilton (1989); Kim & Nelson (1999) | Andrew C. Harvey |
| סוג≠ | Univariate time-series model | Regime-switching time series model | State-space (unobserved components) time series model |
| מקור מכונן≠ | Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 | 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 |
| כינויים≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR | BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM) |
| קשורות≠ | 5 | 5 | 4 |
| תקציר≠ | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). | 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. |
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