Salīdzināt metodes

	Faktoru papildinātais vektorautoregresijas modelis (FAVAR)	Markov režīmu pārslēgšanās modelis (MS-AR / MS-VAR)	Vektora autoregresijas (VAR) modelis
Nozare	Ekonometrija	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model	Regression model
Izcelsmes gads	2005	1989	2005
Autors	Bernanke, Boivin & Eliasz (2005); building on Stock & Watson diffusion indexes	Hamilton (1989); Kim & Nelson (1999)	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Tips	Multivariate time-series model	Regime-switching time series model	Multivariate time-series model
Pirmavots	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.	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Citi nosaukumi	factor-augmented VAR, FAVAR model, Faktör Artırımlı VAR (FAVAR)	regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Saistītās	4	5	4
Kopsavilkums	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.	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).