Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Модел в състояние пространство (Калманов филтър)× | Структурна векторна авторегресия (SVAR)× | Модел на векторна авторегресия (VAR)× | |
|---|---|---|---|
| Област | Иконометрия | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model | Regression model |
| Година на възникване≠ | 1990 | 1980 | 2005 |
| Създател≠ | Harvey; Durbin & Koopman (state space treatment); Kalman filter | Sims (1980); identification schemes by Blanchard & Quah (1989) | Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition |
| Тип≠ | State space time series model | Multivariate time series model | Multivariate time-series model |
| Основополагащ източник≠ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ | Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79(4), 655-673. link ↗ | Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. DOI ↗ |
| Други названия | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) | SVAR, structural vector autoregression, identified VAR, structural VAR model | vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon |
| Свързани≠ | 4 | 5 | 4 |
| Резюме≠ | 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. | Structural VAR extends the reduced-form VAR by imposing economic theory-based restrictions that identify orthogonal structural shocks. This allows researchers to disentangle the causal effects of distinct economic disturbances — such as supply versus demand shocks — and trace their dynamic propagation through a system of variables via impulse response functions and forecast error variance decompositions. | Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005). |
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