เปรียบเทียบวิธี

	Bayesian Vector Autoregression (BVAR)	แบบจำลองปริภูมิสถานะ (ตัวกรองคาลมาน)
สาขาวิชา	เศรษฐมิติ	เศรษฐมิติ
ตระกูล	Regression model	Regression model
ปีกำเนิด	1986	1990
ผู้ริเริ่ม	Litterman (1986); Bańbura, Giannone & Reichlin (2010)	Harvey; Durbin & Koopman (state space treatment); Kalman filter
ประเภท	Bayesian multivariate time-series model	State space time series model
แหล่งต้นตำรับ	Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions—Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25-38.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
ชื่อเรียกอื่น	BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR)	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
ที่เกี่ยวข้อง	5	4
สรุป	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.	A state space model is a general time series framework that describes a series through unobserved (latent) state variables linked by a measurement equation and a transition equation, with the states estimated in real time by the Kalman filter. Developed in the state space tradition of Harvey (1990) and Durbin & Koopman (2012), it nests ARIMA and exponential smoothing as special cases.