Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Анализ панельных данных с изменяющимися во времени параметрами× | Модель пространства состояний (фильтр Калмана)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1960–2003 | 1990 |
| Автор метода≠ | Cheng Hsiao (panel treatment); Kalman (state-space foundation) | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| Тип≠ | Dynamic panel model | State space time series model |
| Основополагающий источник≠ | Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press. ISBN: 978-0521522717 | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ |
| Другие названия | TVP panel model, time-varying coefficient panel model, state-space panel regression, random coefficient panel model | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) |
| Связанные≠ | 5 | 4 |
| Сводка≠ | Time-varying parameter (TVP) panel data analysis extends standard panel regression by allowing the slope coefficients to evolve over time for each unit. Instead of assuming a single fixed or random coefficient, the model lets each unit's relationship between predictors and outcome shift period by period, capturing structural change, learning effects, and heterogeneous dynamics across individuals and time. | 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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