เปรียบเทียบวิธี

	Bayesian Vector Autoregression (BVAR)	FAVAR	การถดถอยกำลังสองน้อยที่สุดสามัญ (OLS)	Threshold and Smooth-Transition VAR
สาขาวิชา	เศรษฐมิติ	เศรษฐมิติ	เศรษฐมิติ	เศรษฐมิติ
ตระกูล	Regression model	Regression model	Regression model	Regression model
ปีกำเนิด	1986	2005	2019	1998
ผู้ริเริ่ม	Litterman (1986); Bańbura, Giannone & Reichlin (2010)	Bernanke, Boivin & Eliasz (2005); building on Stock & Watson diffusion indexes	Wooldridge (textbook treatment); classical least squares	Tsay (multivariate threshold modelling)
ประเภท	Bayesian multivariate time-series model	Multivariate time-series model	Linear regression	Nonlinear multivariate time-series model
แหล่งต้นตำรับ	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	Tsay, R. S. (1998). Testing and Modeling Multivariate Threshold Models. Journal of the American Statistical Association, 93(443), 1188-1202.
ชื่อเรียกอื่น	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	TVAR, STVAR, regime-switching VAR, threshold VAR
ที่เกี่ยวข้อง	5	4	5	5
สรุป	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).	Threshold VAR and Smooth-Transition VAR are nonlinear multivariate time-series models in which the coefficients of a vector autoregression switch between regimes according to a threshold variable. Building on Tsay's 1998 treatment of multivariate threshold models, they capture different dynamic structures across phases such as the business cycle, financial crises, or policy differences.