Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Laika mainīgo parametru modeļa ar fiksētajiem efektiem× | Valsts telpas modelis (Kalmana filtrs)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1975-1995 | 1990 |
| Autors≠ | Hsiao (1975); Pesaran & Smith (1995) | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| Tips≠ | Panel regression with time-varying slopes | State space time series model |
| Pirmavots≠ | Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. ISBN: 9781107038875 | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ |
| Citi nosaukumi | TVP-FE model, time-varying coefficients fixed effects, TVP panel model, locally time-varying fixed effects | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) |
| Saistītās≠ | 2 | 4 |
| Kopsavilkums≠ | The time-varying parameter fixed effects (TVP-FE) model extends the classical two-way fixed effects panel regression by allowing one or more slope coefficients to change over time while still controlling for unobserved individual heterogeneity. It is used when the effect of a predictor on an outcome is not constant across the time dimension of a panel dataset. | 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. |
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