เปรียบเทียบวิธี

	แบบจำลองปริภูมิสถานะ (ตัวกรองคาลมาน)	แบบจำลองมาร์คอฟสลับระบอบ (MS-AR / MS-VAR)
สาขาวิชา	เศรษฐมิติ	เศรษฐมิติ
ตระกูล	Regression model	Regression model
ปีกำเนิด	1990	1989
ผู้ริเริ่ม	Harvey; Durbin & Koopman (state space treatment); Kalman filter	Hamilton (1989); Kim & Nelson (1999)
ประเภท	State space 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.	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)	regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR
ที่เกี่ยวข้อง	4	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.	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.