Сравнение на методи

	Факторно-допълнена векторна авторегресия (FAVAR)	Метод на най-малките квадрати (МНК)	Векторна авторегресия с праг и плавен преход (TVAR / STVAR)	Модел на векторна авторегресия (VAR)
Област	Иконометрия	Иконометрия	Иконометрия	Иконометрия
Семейство	Regression model	Regression model	Regression model	Regression model
Година на възникване	2005	2019	1998	2005
Създател	Bernanke, Boivin & Eliasz (2005); building on Stock & Watson diffusion indexes	Wooldridge (textbook treatment); classical least squares	Tsay (multivariate threshold modelling)	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Тип	Multivariate time-series model	Linear regression	Nonlinear multivariate time-series model	Multivariate time-series model
Основополагащ източник	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.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	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.
Други названия	factor-augmented VAR, FAVAR model, Faktör Artırımlı VAR (FAVAR)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	TVAR, STVAR, regime-switching VAR, threshold VAR	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Свързани	4	5	5	4
Резюме	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	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).