Compară metode

	Modelul Vectorial Autoregresiv Augmentat cu Factori (FAVAR)	Modelul Markov cu comutare de regim (MS-AR / MS-VAR)	Regresia prin metoda celor mai mici pătrate ordinare (OLS)
Domeniu	Econometrie	Econometrie	Econometrie
Familie	Regression model	Regression model	Regression model
Anul apariției	2005	1989	2019
Autorul original	Bernanke, Boivin & Eliasz (2005); building on Stock & Watson diffusion indexes	Hamilton (1989); Kim & Nelson (1999)	Wooldridge (textbook treatment); classical least squares
Tip	Multivariate time-series model	Regime-switching time series model	Linear regression
Sursa seminală	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
Denumiri alternative	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
Înrudite	4	5	5
Rezumat	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).