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| HP Filter× | Модел в състояние пространство (Калманов филтър)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство≠ | Process / pipeline | Regression model |
| Година на възникване≠ | 1997 | 1990 |
| Създател≠ | Robert Hodrick & Edward Prescott | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| Тип≠ | Penalized least-squares smoother | State space time series model |
| Основополагащ източник≠ | Hodrick, R. J., & Prescott, E. C. (1997). Postwar U.S. business cycles: An empirical investigation. Journal of Money, Credit and Banking, 29(1), 1–16. DOI ↗ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ |
| Други названия | Hodrick-Prescott Filter, HP Decomposition, Trend-Cycle Filter, HP Filtresi | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) |
| Свързани≠ | 3 | 4 |
| Резюме≠ | The Hodrick-Prescott (HP) filter is a penalized least-squares technique used in macroeconomics and empirical finance to decompose a time series into a smooth long-run trend component and a short-run cyclical component. Introduced by Hodrick and Prescott (1997) using postwar U.S. business cycle data, it has become one of the most widely applied filters in business cycle analysis, monetary policy research, and applied econometrics. | 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 от данни ↗ |
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