Comparar métodos

	VAR com Parâmetros Variáveis no Tempo (TVP-VAR)	Modelo de Vetores Autorregressivos (VAR)
Área	Econometria	Econometria
Família	Regression model	Regression model
Ano de origem	2005	2005
Autor original	Giorgio Primiceri	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Tipo	Bayesian state-space model	Multivariate time-series model
Fonte seminal	Primiceri, G. E. (2005). Time varying structural vector autoregressions and monetary policy. Review of Economic Studies, 72(3), 821–852.	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Outros nomes	Time-Varying Parameter Vector Autoregression, TVP-SVAR, Stochastic Coefficient VAR, Zamana Göre Değişen Parametreli VAR	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Relacionados	2	4
Resumo	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.	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).