Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Обикновени най-малки квадрати с променливи във времето параметри (TVP-OLS)× | Модел в състояние пространство (Калманов филтър)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1976 | 1990 |
| Създател≠ | Cooley & Prescott (1976); further developed by Harvey (1990) | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| Тип≠ | Time-series regression with evolving coefficients | State space time series model |
| Основополагащ източник≠ | Cooley, T. F., & Prescott, E. C. (1976). Estimation in the Presence of Stochastic Parameter Variation. Econometrica, 44(1), 167–184. DOI ↗ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ |
| Други названия | TVP-OLS, time-varying coefficient regression, rolling OLS, locally weighted OLS | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) |
| Свързани | 4 | 4 |
| Резюме≠ | Time-Varying Parameter OLS extends classical ordinary least squares to allow regression coefficients to change over time. Instead of assuming fixed slopes throughout the sample, the model treats each coefficient as a stochastic process, tracking how economic relationships evolve — making it well-suited for analysing structural change in time-series data. | 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 ↗ |
|
|