Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель векторной авторегрессии с изменяющимися во времени параметрами (TVP-VAR)× | Модель пространства состояний (фильтр Калмана)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2005 | 1990 |
| Автор метода≠ | Primiceri (2005); Cogley & Sargent (2001, 2005) | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| Тип≠ | Multivariate time-series model with drifting coefficients | State space time series model |
| Основополагающий источник≠ | Primiceri, G. E. (2005). Time varying structural vector autoregressions and monetary policy. Review of Economic Studies, 72(3), 821-852. DOI ↗ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ |
| Другие названия | TVP-VAR, time-varying VAR, TV-VAR, drifting-coefficient VAR | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) |
| Связанные≠ | 6 | 4 |
| Сводка≠ | The Time-Varying Parameter VAR (TVP-VAR) model extends the standard vector autoregression by allowing the coefficients and error covariances to evolve gradually over time. Estimated via Bayesian methods and MCMC simulation, it captures how dynamic relationships between macroeconomic or financial variables shift across different economic regimes without requiring pre-specified break points. | 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. |
| ScholarGateНабор данных ↗ |
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