विधियों की तुलना करें

	फैक्टर-ऑगमेंटेड वेक्टर ऑटोरिग्रेशन (FAVAR)	मार्कोव रेजीम-स्विचिंग मॉडल (MS-AR / MS-VAR)	साधारण न्यूनतम वर्ग (OLS) समाश्रयण	वेक्टर ऑटोरिग्रेशन (VAR) मॉडल
क्षेत्र	अर्थमिति	अर्थमिति	अर्थमिति	अर्थमिति
परिवार	Regression model	Regression model	Regression model	Regression model
उद्भव वर्ष	2005	1989	2019	2005
प्रवर्तक	Bernanke, Boivin & Eliasz (2005); building on Stock & Watson diffusion indexes	Hamilton (1989); Kim & Nelson (1999)	Wooldridge (textbook treatment); classical least squares	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
प्रकार	Multivariate time-series model	Regime-switching time series model	Linear regression	Multivariate time-series model
मौलिक स्रोत	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	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
उपनाम	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	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
संबंधित	4	5	5	4
सारांश	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).	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).