Порівняння методів

	Байєсівська OLS (Байєсівська звичайна регресія методом найменших квадратів)	Байєсівська модель векторної авторегресії (BVAR)
Галузь	Економетрика	Економетрика
Родина	Regression model	Regression model
Рік появи	1971	1984
Автор методу	Arnold Zellner	Doan, Litterman & Sims
Тип	Bayesian linear regression	Multivariate time-series model
Основоположне джерело	Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley. ISBN: 978-0471169376	Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100.
Інші назви	Bayesian linear regression, Bayesian normal regression, BLR, Bayesian least squares	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model
Пов'язані	5	5
Підсумок	Bayesian OLS combines the classical linear regression likelihood with prior distributions over the coefficients and error variance. Rather than reporting point estimates, it produces full posterior distributions that quantify both estimated effects and their uncertainty. The approach is especially valuable when prior knowledge is available or when samples are small.	The Bayesian Vector Autoregression (BVAR) model extends the classical VAR framework by incorporating prior beliefs about the model coefficients. Priors — most commonly the Minnesota prior — shrink VAR coefficients toward economically sensible values, dramatically reducing overfitting and improving out-of-sample forecast accuracy even when the number of variables is large.