Сравнение на методи

	Динамичен стохастичен общ равновесен модел (DSGE)	Модел в състояние пространство (Калманов филтър)
Област	Иконометрия	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	2007	1990
Създател	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
Основополагащ източник	Smets, F. & Wouters, R. (2007). Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach. American Economic Review, 97(3), 586–606.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
Други названия	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)
Свързани	5	4
Резюме	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.