Salīdzināt metodes

	Bayesiešu ARIMA modelis	Bayesiešu VAR modelis (BVAR)
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1970s (ARIMA); Bayesian extension prominent from 1990s	1984
Autors	Pole, West & Harrison (Bayesian treatment); Box & Jenkins (ARIMA foundation)	Doan, Litterman & Sims
Tips	Bayesian time series model	Multivariate time-series model
Pirmavots	Pole, A., West, M., & Harrison, J. (1994). Applied Bayesian Forecasting and Time Series Analysis. Chapman & Hall. ISBN: 978-0412416903	Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100.
Citi nosaukumi	Bayesian ARIMA, BARIMA, Bayesian Box-Jenkins model, Bayesian integrated time series model	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model
Saistītās	6	5
Kopsavilkums	The Bayesian ARIMA model combines the classical Box-Jenkins ARIMA framework with Bayesian inference. Instead of obtaining single point estimates for autoregressive and moving average parameters, it places prior distributions over them and uses observed data to update beliefs into a full posterior distribution, enabling coherent uncertainty quantification and probabilistic forecasting.	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.