Сравнение на методи

	Байесов модел на авторегресия (AR)	Байесов модел на ARMA
Област	Иконометрия	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	1971	1970s–1980s
Създател	Arnold Zellner; foundational Bayesian time-series work by West & Harrison	Box & Jenkins (classical ARMA); Bayesian treatment developed through work of Zellner, Geweke, and others in 1970s–1980s
Тип	Bayesian time-series model	Bayesian time series model
Основополагащ източник	Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley. ISBN: 978-0471169376	Geweke, J., & Meese, R. (1981). Estimating regression models of finite but unknown order. International Economic Review, 22(1), 55–70.
Други названия	Bayesian autoregressive model, BAR model, Bayesian AR, Bayesian time-series autoregression	Bayesian ARMA, B-ARMA, Bayesian autoregressive moving average, ARMA with Bayesian inference
Свързани	6	6
Резюме	The Bayesian AR model estimates an autoregressive time-series process by combining a likelihood derived from the AR structure with prior distributions over the lag coefficients and error variance. Rather than producing single point estimates, it yields full posterior distributions, enabling principled uncertainty quantification and probabilistic forecasting.	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.