مقایسهٔ روشها
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| مدل تصحیح خطای برداری با پارامترهای متغیر با زمان (TVP-VECM)× | مدل فضای حالت (فیلتر کالمن)× | |
|---|---|---|
| حوزه | اقتصادسنجی | اقتصادسنجی |
| خانواده | Regression model | Regression model |
| سال پیدایش≠ | 1999–2010 | 1990 |
| پدیدآور≠ | Park & Hahn (1999); extended by Bierens & Martins (2010) | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| نوع≠ | Dynamic multivariate time-series model | State space time series model |
| منبع بنیادین≠ | Park, J. Y., & Hahn, S. B. (1999). Cointegrating regressions with time varying coefficients. Econometric Theory, 15(5), 664–703. DOI ↗ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ |
| نامهای دیگر | TVP-VECM, time-varying VECM, TVP cointegration model, dynamic VECM with drifting coefficients | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) |
| مرتبط≠ | 3 | 4 |
| خلاصه≠ | The Time-Varying Parameter Vector Error Correction Model extends the standard VECM by allowing the adjustment speeds, cointegrating vectors, and short-run dynamics to drift over time. It captures long-run cointegrating relationships among integrated series while accommodating structural change, evolving policy regimes, and shifting economic relationships within a unified state-space framework. | 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. |
| ScholarGateمجموعهداده ↗ |
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