Сравнение методов

	Байесовская модель ARMA	Модель ARIMA (авторегрессионная интегрированная скользящая средняя)
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1970s–1980s	1970
Автор метода	Box & Jenkins (classical ARMA); Bayesian treatment developed through work of Zellner, Geweke, and others in 1970s–1980s	George Box and Gwilym Jenkins
Тип	Bayesian time series model	Time series forecasting model
Основополагающий источник	Geweke, J., & Meese, R. (1981). Estimating regression models of finite but unknown order. International Economic Review, 22(1), 55–70.	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.
Другие названия	Bayesian ARMA, B-ARMA, Bayesian autoregressive moving average, ARMA with Bayesian inference	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)
Связанные	6	6
Сводка	The Bayesian ARMA model applies Bayesian inference to the classical autoregressive moving average framework for stationary univariate time series. Rather than producing single point estimates for the AR and MA parameters, it yields full posterior distributions, naturally incorporating prior knowledge and providing coherent uncertainty quantification over forecasts and impulse responses.	The ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics.