Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Тест Хаусмана для моделей с меняющимися во времени параметрами× | Модель пространства состояний (фильтр Калмана)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1978 (Hausman); TVP extension developed through 1980s–2000s | 1990 |
| Автор метода≠ | Hausman (1978) specification test framework extended to time-varying parameter settings | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| Тип≠ | Specification / endogeneity test | State space time series model |
| Основополагающий источник≠ | Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251-1271. DOI ↗ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ |
| Другие названия | TVP Hausman test, time-varying Hausman specification test, Hausman test with time-varying parameters, TVP endogeneity test | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) |
| Связанные≠ | 3 | 4 |
| Сводка≠ | The time-varying parameter Hausman test extends Hausman's (1978) classic specification test to models whose coefficients are allowed to evolve over time. It compares an efficient estimator (e.g., OLS or GLS assuming constant parameters) with a consistent estimator from a time-varying parameter model, using the contrast between them to detect parameter instability or endogeneity in dynamic settings. | 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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