विधियों की तुलना करें

	बायेसियन वेक्टर ऑटोरिग्रेशन (BVAR)	साधारण न्यूनतम वर्ग (OLS) समाश्रयण
क्षेत्र	अर्थमिति	अर्थमिति
परिवार	Regression model	Regression model
उद्भव वर्ष	1986	2019
प्रवर्तक	Litterman (1986); Bańbura, Giannone & Reichlin (2010)	Wooldridge (textbook treatment); classical least squares
प्रकार	Bayesian multivariate time-series model	Linear regression
मौलिक स्रोत	Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions—Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25-38.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
उपनाम	BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
संबंधित	5	5
सारांश	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.	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).