Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Авторегрессионная модель с изменяющимися во времени параметрами (TVP-AR)× | Модель пространства состояний (фильтр Калмана)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1976–2005 | 1990 |
| Автор метода≠ | Cooley & Prescott (1976); further developed by Kim & Nelson (1999) and Cogley & Sargent (2001, 2005) | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| Тип≠ | Time-series model with drifting coefficients | State space time series model |
| Основополагающий источник≠ | Cogley, T., & Sargent, T. J. (2005). Drifts and volatilities: Monetary policies and outcomes in the post WWII US. Review of Economic Dynamics, 8(2), 262-302. DOI ↗ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ |
| Другие названия | TVP-AR, time-varying AR, state-space AR with drifting coefficients, random-walk coefficient AR | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) |
| Связанные | 4 | 4 |
| Сводка≠ | The Time-Varying Parameter Autoregressive (TVP-AR) model extends the classical AR model by allowing its autoregressive coefficients to drift over time, typically as a random walk. Cast as a state-space system, the model captures gradual structural change in the dynamics of a univariate time series without imposing a fixed break date. | 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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