방법 비교

	상태 공간 모형 (칼만 필터)	마르코프 정권 전환 모형 (MS-AR / MS-VAR)	구조 시계열 모형 (기본 구조 모형)
분야	계량경제학	계량경제학	계량경제학
계열	Regression model	Regression model	Regression model
기원 연도	1990	1989	1990
창시자	Harvey; Durbin & Koopman (state space treatment); Kalman filter	Hamilton (1989); Kim & Nelson (1999)	Andrew C. Harvey
유형	State space time series model	Regime-switching time series model	State-space (unobserved components) time series model
원전	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.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737
별칭	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)	regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR	BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM)
관련	4	5	4
요약	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.	The Structural Time Series Model, in its Basic Structural Model (BSM) form, is Andrew Harvey's state-space approach that decomposes a series into separate stochastic trend, seasonal, cyclical, and irregular components. Developed in Harvey's 1990 treatment, it is prized for interpretability and component decomposition where ARIMA only delivers a black-box fit.