Confronta i metodi

	Modello Autoregressivo (AR) Bayesiano	Modello ARMA (Autoregressive Moving Average)
Campo	Econometria	Econometria
Famiglia	Regression model	Regression model
Anno di origine	1971	1970
Ideatore	Arnold Zellner; foundational Bayesian time-series work by West & Harrison	George E. P. Box and Gwilym M. Jenkins
Tipo	Bayesian time-series model	Time series model
Fonte seminale	Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley. ISBN: 978-0471169376	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.
Alias	Bayesian autoregressive model, BAR model, Bayesian AR, Bayesian time-series autoregression	ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q)
Correlati	6	5
Sintesi	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 ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a moving average part that accounts for past q error terms. It is the foundational framework of the Box-Jenkins methodology for univariate time series modelling and short-run forecasting.