เปรียบเทียบวิธี

	แบบจำลอง ARIMA (Autoregressive Integrated Moving Average)	Bayesian Vector Autoregression (BVAR)
สาขาวิชา	เศรษฐมิติ	เศรษฐมิติ
ตระกูล	Regression model	Regression model
ปีกำเนิด	2015	1986
ผู้ริเริ่ม	Box & Jenkins (Box-Jenkins methodology)	Litterman (1986); Bańbura, Giannone & Reichlin (2010)
ประเภท	Univariate time-series model	Bayesian multivariate time-series model
แหล่งต้นตำรับ	Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021	Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions—Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25-38.
ชื่อเรียกอื่น	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR)
ที่เกี่ยวข้อง	5	5
สรุป	ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).	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.