Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Modelul spațiului de stare (Filtrul Kalman)× | Modelul ARIMA (Autoregresiv Integrat cu Medii Mobile)× | Autoregresia Vectorial Bayesiană (BVAR)× | |
|---|---|---|---|
| Domeniu | Econometrie | Econometrie | Econometrie |
| Familie | Regression model | Regression model | Regression model |
| Anul apariției≠ | 1990 | 2015 | 1986 |
| Autorul original≠ | Harvey; Durbin & Koopman (state space treatment); Kalman filter | Box & Jenkins (Box-Jenkins methodology) | Litterman (1986); Bańbura, Giannone & Reichlin (2010) |
| Tip≠ | State space time series model | Univariate time-series model | Bayesian multivariate time-series model |
| Sursa seminală≠ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ | 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 | Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions—Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25-38. DOI ↗ |
| Denumiri alternative≠ | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR) |
| Înrudite≠ | 4 | 5 | 5 |
| Rezumat≠ | 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. | 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). | 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. |
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