Salīdzināt metodes

	Beijesas slīdošā vidējā (MA) modelis	Bayesiešu autoregresijas (AR) modelis
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1970s–1997	1971
Autors	Bayesian framework applied to Box-Jenkins MA models; West & Harrison (1997) canonical treatment	Arnold Zellner; foundational Bayesian time-series work by West & Harrison
Tips	Bayesian time series model	Bayesian time-series model
Pirmavots	West, M., & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259	Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley. ISBN: 978-0471169376
Citi nosaukumi	Bayesian MA, Bayesian moving average, BMA time series, MA model with Bayesian estimation	Bayesian autoregressive model, BAR model, Bayesian AR, Bayesian time-series autoregression
Saistītās	6	6
Kopsavilkums	The Bayesian MA model estimates a moving average time series model within a fully Bayesian framework, placing prior distributions on the MA parameters and error variance and updating them via Bayes' theorem. This approach yields full posterior distributions over model parameters and produces probabilistic forecasts with coherent uncertainty quantification.	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.