Порівняння методів

	Bayesian ARIMA Model	Байєсівська модель векторної авторегресії (BVAR)
Галузь	Економетрика	Економетрика
Родина	Regression model	Regression model
Рік появи	1970s (ARIMA); Bayesian extension prominent from 1990s	1984
Автор методу	Pole, West & Harrison (Bayesian treatment); Box & Jenkins (ARIMA foundation)	Doan, Litterman & Sims
Тип	Bayesian time series model	Multivariate time-series model
Основоположне джерело	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.
Інші назви	Bayesian ARIMA, BARIMA, Bayesian Box-Jenkins model, Bayesian integrated time series model	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model
Пов'язані	6	5
Підсумок	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.