Сравнение методов

	Байесовская модель ARMA	Байесовский МНК (Байесовская линейная регрессия методом наименьших квадратов)
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1970s–1980s	1971
Автор метода	Box & Jenkins (classical ARMA); Bayesian treatment developed through work of Zellner, Geweke, and others in 1970s–1980s	Arnold Zellner
Тип	Bayesian time series model	Bayesian linear regression
Основополагающий источник	Geweke, J., & Meese, R. (1981). Estimating regression models of finite but unknown order. International Economic Review, 22(1), 55–70.	Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley. ISBN: 978-0471169376
Другие названия	Bayesian ARMA, B-ARMA, Bayesian autoregressive moving average, ARMA with Bayesian inference	Bayesian linear regression, Bayesian normal regression, BLR, Bayesian least squares
Связанные	6	5
Сводка	The Bayesian ARMA model applies Bayesian inference to the classical autoregressive moving average framework for stationary univariate time series. Rather than producing single point estimates for the AR and MA parameters, it yields full posterior distributions, naturally incorporating prior knowledge and providing coherent uncertainty quantification over forecasts and impulse responses.	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.