Сравнение методов

	Динамическая стохастическая модель общего равновесия (DSGE)	Структурная векторная авторегрессия (SVAR)	Модель векторной авторегрессии (VAR)
Область	Эконометрика	Эконометрика	Эконометрика
Семейство	Regression model	Regression model	Regression model
Год появления	2007	1980	2005
Автор метода	Smets & Wouters; An & Schorfheide (Bayesian DSGE estimation)	Sims (1980); identification schemes by Blanchard & Quah (1989)	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Тип	Micro-founded macroeconomic general equilibrium model	Multivariate time series model	Multivariate time-series model
Основополагающий источник	Smets, F. & Wouters, R. (2007). Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach. American Economic Review, 97(3), 586–606.	Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79(4), 655-673.	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Другие названия	DSGE, dynamic stochastic general equilibrium, micro-founded macroeconomic model, Dinamik Stokastik Genel Denge Modeli (DSGE)	SVAR, structural vector autoregression, identified VAR, structural VAR model	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Связанные	5	5	4
Сводка	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.	Structural VAR extends the reduced-form VAR by imposing economic theory-based restrictions that identify orthogonal structural shocks. This allows researchers to disentangle the causal effects of distinct economic disturbances — such as supply versus demand shocks — and trace their dynamic propagation through a system of variables via impulse response functions and forecast error variance decompositions.	Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005).