Сравнение методов

	Векторная авторегрессия с время-меняющимися параметрами (TVP-VAR)	Структурная векторная авторегрессия (SVAR)
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	2005	1980
Автор метода	Giorgio Primiceri	Christopher Sims
Тип	Bayesian state-space model	Structural multivariate time-series model
Основополагающий источник	Primiceri, G. E. (2005). Time varying structural vector autoregressions and monetary policy. Review of Economic Studies, 72(3), 821–852.	Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1), 1–48.
Другие названия	Time-Varying Parameter Vector Autoregression, TVP-SVAR, Stochastic Coefficient VAR, Zamana Göre Değişen Parametreli VAR	Structural VAR, Identified VAR, SVAR Model, Yapısal Vektör Otoregresyon
Связанные	2	2
Сводка	TVP-VAR is a Bayesian multivariate time-series model in which both the VAR coefficients and the shock covariance matrix are allowed to evolve continuously over time as random walks. Introduced by Primiceri (2005) to study U.S. monetary policy transmission, the model captures structural changes and regime shifts without requiring ex-ante knowledge of when breaks occurred, making it indispensable for macroeconomics, finance, and any setting where economic relationships are suspected to be unstable across time.	Structural Vector Autoregression (SVAR) is a multivariate time-series model, developed by Christopher Sims (1980), that extends the reduced-form VAR by imposing economically motivated identifying restrictions on contemporaneous relationships among variables. SVAR enables researchers to isolate orthogonal structural shocks and trace their causal dynamic effects through impulse response functions and forecast error variance decompositions, making it a cornerstone of modern empirical macroeconomics.