Confronta i metodi

	Vector Autoregressione Aumentato da Fattori (FAVAR)	Modello a commutazione di regime di Markov (MS-AR / MS-VAR)	Regression with Ordinary Least Squares (OLS)	VAR a Soglia e a Transizione Graduale (TVAR / STVAR)
Campo	Econometria	Econometria	Econometria	Econometria
Famiglia	Regression model	Regression model	Regression model	Regression model
Anno di origine	2005	1989	2019	1998
Ideatore	Bernanke, Boivin & Eliasz (2005); building on Stock & Watson diffusion indexes	Hamilton (1989); Kim & Nelson (1999)	Wooldridge (textbook treatment); classical least squares	Tsay (multivariate threshold modelling)
Tipo	Multivariate time-series model	Regime-switching time series model	Linear regression	Nonlinear multivariate time-series model
Fonte seminale	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.	Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384.	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.
Alias	factor-augmented VAR, FAVAR model, Faktör Artırımlı VAR (FAVAR)	regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	TVAR, STVAR, regime-switching VAR, threshold VAR
Correlati	4	5	5	5
Sintesi	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.	The Markov regime-switching model lets the parameters of a time series change probabilistically across hidden regimes governed by a Markov chain. Introduced by Hamilton (1989) and developed further by Kim and Nelson (1999), it automatically detects business-cycle phases such as expansions and contractions.	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.