Bandingkan metode

	Autoregresi Vektor Bayesian (BVAR)	Autoregresi Vektor yang Diperkaya Faktor (FAVAR)	Regresi Kuadrat Terkecil Biasa (Ordinary Least Squares - OLS)
Bidang	Ekonometrika	Ekonometrika	Ekonometrika
Keluarga	Regression model	Regression model	Regression model
Tahun asal	1986	2005	2019
Pencetus	Litterman (1986); Bańbura, Giannone & Reichlin (2010)	Bernanke, Boivin & Eliasz (2005); building on Stock & Watson diffusion indexes	Wooldridge (textbook treatment); classical least squares
Tipe	Bayesian multivariate time-series model	Multivariate time-series model	Linear regression
Sumber perintis	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.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR)	factor-augmented VAR, FAVAR model, Faktör Artırımlı VAR (FAVAR)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Terkait	5	4	5
Ringkasan	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.	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).