เปรียบเทียบวิธี

	แบบจำลองปริภูมิสถานะ (ตัวกรองคาลมาน)	Bayesian Vector Autoregression (BVAR)	แบบจำลองมาร์คอฟสลับระบอบ (MS-AR / MS-VAR)
สาขาวิชา	เศรษฐมิติ	เศรษฐมิติ	เศรษฐมิติ
ตระกูล	Regression model	Regression model	Regression model
ปีกำเนิด	1990	1986	1989
ผู้ริเริ่ม	Harvey; Durbin & Koopman (state space treatment); Kalman filter	Litterman (1986); Bańbura, Giannone & Reichlin (2010)	Hamilton (1989); Kim & Nelson (1999)
ประเภท	State space time series model	Bayesian multivariate time-series model	Regime-switching time series model
แหล่งต้นตำรับ	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.	Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions—Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25-38.	Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384.
ชื่อเรียกอื่น	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)	BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR)	regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR
ที่เกี่ยวข้อง	4	5	5
สรุป	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.	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.	The Markov regime-switching model lets the parameters of a time series change probabilistically across hidden regimes governed by a Markov chain. Introduced by Hamilton (1989) and developed further by Kim and Nelson (1999), it automatically detects business-cycle phases such as expansions and contractions.