Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Модель динамічної стохастичної загальної рівноваги (DSGE)× | Модель простір-стан (фільтр Калмана)× | |
|---|---|---|
| Галузь | Економетрика | Економетрика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 2007 | 1990 |
| Автор методу≠ | Smets & Wouters; An & Schorfheide (Bayesian DSGE estimation) | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| Тип≠ | Micro-founded macroeconomic general equilibrium model | State space time series model |
| Основоположне джерело≠ | Smets, F. & Wouters, R. (2007). Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach. American Economic Review, 97(3), 586–606. DOI ↗ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ |
| Інші назви | DSGE, dynamic stochastic general equilibrium, micro-founded macroeconomic model, Dinamik Stokastik Genel Denge Modeli (DSGE) | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) |
| Пов'язані≠ | 5 | 4 |
| Підсумок≠ | A DSGE model is a micro-founded macroeconomic general equilibrium model that combines the optimising decisions of households, firms, and government under rational expectations. Popularised for empirical policy work by Smets and Wouters (2007) and given its Bayesian estimation framework by An and Schorfheide (2007), it is the standard tool for central-bank policy analysis, fiscal-shock simulation, and the study of business-cycle fluctuations. | 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. |
| ScholarGateНабір даних ↗ |
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