Сравнение на методи

	SARIMAX	Байесов векторна авторегресия (BVAR)
Област	Иконометрия	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	2015	1986
Създател	Box & Jenkins (ARIMA framework); SARIMAX extension with exogenous regressors	Litterman (1986); Bańbura, Giannone & Reichlin (2010)
Тип	Seasonal time-series regression model	Bayesian multivariate time-series model
Основополагащ източник	Hyndman, R. J. & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts.	Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions—Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25-38.
Други названия	seasonal ARIMA with exogenous variables, SARIMA with regressors, ARIMAX, SARIMAX — Dışsal Değişkenli Mevsimsel ARIMA	BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR)
Свързани	4	5
Резюме	SARIMAX extends the seasonal ARIMA (Box-Jenkins) model by adding exogenous explanatory variables, so it can capture the effect of holidays, economic indicators, or policy variables on a time series. It combines non-seasonal and seasonal autoregressive and moving-average dynamics with external regressors, and is estimated by maximum likelihood in state-space form.	Bayesian VAR adds Minnesota or other prior distributions to a vector autoregressive model to control over-parameterisation. Introduced by Litterman (1986) and extended to high dimensions by Bańbura, Giannone and Reichlin (2010), it outperforms classical VAR on short series and high-dimensional macroeconomic forecasts.