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Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Modèle de données de panel dynamique à paramètres variant dans le temps× | Modèle d'espace d'états (Filtre de Kalman)× | |
|---|---|---|
| Domaine | Économétrie | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1990s–2000s | 1990 |
| Auteur d'origine≠ | Hsiao, Pesaran, and related panel time-series literature | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| Type≠ | Dynamic panel model with time-varying coefficients | State space time series model |
| Source fondatrice≠ | Canova, F., & Ciccarelli, M. (2009). Estimating multicountry VAR models. International Economic Review, 50(3), 929-959. DOI ↗ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ |
| Alias | TVP dynamic panel model, time-varying coefficient panel model, TVP-DPD model, state-space dynamic panel model | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) |
| Apparentées≠ | 2 | 4 |
| Résumé≠ | The time-varying parameter dynamic panel data model combines lagged dependent variables with coefficients that evolve over time across panel units. It extends conventional dynamic panel models by allowing slope parameters to shift across periods, making it well-suited for studying structural change, heterogeneous adjustment dynamics, and parameter instability in macro-panels and cross-country datasets. | 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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