השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| אוטו-רגרסיה וקטורית בייסיאנית (BVAR)× | מודל מרחב מצב (מסנן קלמן)× | |
|---|---|---|
| תחום | אקונומטריקה | אקונומטריקה |
| משפחה | Regression model | Regression model |
| שנת המקור≠ | 1986 | 1990 |
| הוגה השיטה≠ | Litterman (1986); Bańbura, Giannone & Reichlin (2010) | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| סוג≠ | Bayesian multivariate time-series model | State space time series model |
| מקור מכונן≠ | 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. DOI ↗ |
| כינויים | BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR) | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) |
| קשורות≠ | 5 | 4 |
| תקציר≠ | 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. | 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. |
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