Порівняння методів

	Векторна авторегресія з часовими параметрами (TVP-VAR)	Структурна векторна авторегресія (SVAR)	Модель векторної авторегресії (VAR)
Галузь	Економетрика	Економетрика	Економетрика
Родина	Regression model	Regression model	Regression model
Рік появи	2005	1980	2005
Автор методу	Giorgio Primiceri	Christopher Sims	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Тип	Bayesian state-space model	Structural multivariate time-series model	Multivariate time-series model
Основоположне джерело	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.	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Інші назви	Time-Varying Parameter Vector Autoregression, TVP-SVAR, Stochastic Coefficient VAR, Zamana Göre Değişen Parametreli VAR	Structural VAR, Identified VAR, SVAR Model, Yapısal Vektör Otoregresyon	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Пов'язані	2	2	4
Підсумок	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.	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).