So sánh phương pháp

	Mô hình Tự hồi quy Vector Tăng cường Nhân tố (FAVAR)	Mô hình VAR Ngưỡng và VAR Chuyển đổi Mượt (TVAR / STVAR)	Mô hình Tự hồi quy Vector (VAR)
Lĩnh vực	Kinh tế lượng	Kinh tế lượng	Kinh tế lượng
Họ	Regression model	Regression model	Regression model
Năm ra đời	2005	1998	2005
Người khởi xướng	Bernanke, Boivin & Eliasz (2005); building on Stock & Watson diffusion indexes	Tsay (multivariate threshold modelling)	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Loại	Multivariate time-series model	Nonlinear multivariate time-series model	Multivariate time-series model
Công trình gốc	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.	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Tên gọi khác	factor-augmented VAR, FAVAR model, Faktör Artırımlı VAR (FAVAR)	TVAR, STVAR, regime-switching VAR, threshold VAR	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Liên quan	4	5	4
Tóm tắt	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.	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).