Сравнение на методи

	Байесов векторна авторегресия (BVAR)	Факторно-допълнена векторна авторегресия (FAVAR)	Векторна авторегресия с праг и плавен преход (TVAR / STVAR)
Област	Иконометрия	Иконометрия	Иконометрия
Семейство	Regression model	Regression model	Regression model
Година на възникване	1986	2005	1998
Създател	Litterman (1986); Bańbura, Giannone & Reichlin (2010)	Bernanke, Boivin & Eliasz (2005); building on Stock & Watson diffusion indexes	Tsay (multivariate threshold modelling)
Тип	Bayesian multivariate time-series model	Multivariate time-series model	Nonlinear multivariate time-series model
Основополагащ източник	Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions—Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25-38.	Bernanke, B. S., Boivin, J. & Eliasz, P. (2005). Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach. The Quarterly Journal of Economics, 120(1), 387-422.	Tsay, R. S. (1998). Testing and Modeling Multivariate Threshold Models. Journal of the American Statistical Association, 93(443), 1188-1202.
Други названия	BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR)	factor-augmented VAR, FAVAR model, Faktör Artırımlı VAR (FAVAR)	TVAR, STVAR, regime-switching VAR, threshold VAR
Свързани	5	4	5
Резюме	Bayesian VAR adds Minnesota or other prior distributions to a vector autoregressive model to control over-parameterisation. Introduced by Litterman (1986) and extended to high dimensions by Bańbura, Giannone and Reichlin (2010), it outperforms classical VAR on short series and high-dimensional macroeconomic forecasts.	FAVAR is a multivariate time-series model that first compresses information from a very large set of variables into a few common factors, then includes those factors alongside the observed variables in a vector autoregression. It was introduced by Bernanke, Boivin and Eliasz in 2005 to study monetary policy using hundreds of macroeconomic indicators at once.	Threshold VAR and Smooth-Transition VAR are nonlinear multivariate time-series models in which the coefficients of a vector autoregression switch between regimes according to a threshold variable. Building on Tsay's 1998 treatment of multivariate threshold models, they capture different dynamic structures across phases such as the business cycle, financial crises, or policy differences.