השוואת שיטות

	אוטו-רגרסיה וקטורית בייסיאנית (BVAR)	מודל אוטורגרסיה וקטורית מוגבר-גורמים (FAVAR)
תחום	אקונומטריקה	אקונומטריקה
משפחה	Regression model	Regression model
שנת המקור	1986	2005
הוגה השיטה	Litterman (1986); Bańbura, Giannone & Reichlin (2010)	Bernanke, Boivin & Eliasz (2005); building on Stock & Watson diffusion indexes
סוג	Bayesian multivariate time-series model	Multivariate time-series model
מקור מכונן	Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions—Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25-38.	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.
כינויים	BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR)	factor-augmented VAR, FAVAR model, Faktör Artırımlı VAR (FAVAR)
קשורות	5	4
תקציר	Bayesian VAR adds Minnesota or other prior distributions to a vector autoregressive model to control over-parameterisation. Introduced by Litterman (1986) and extended to high dimensions by Bańbura, Giannone and Reichlin (2010), it outperforms classical VAR on short series and high-dimensional macroeconomic forecasts.	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.