방법 비교

	상태 공간 모형 (칼만 필터)	ARIMA (Autoregressive Integrated Moving Average) 모형	마르코프 정권 전환 모형 (MS-AR / MS-VAR)
분야	계량경제학	계량경제학	계량경제학
계열	Regression model	Regression model	Regression model
기원 연도	1990	2015	1989
창시자	Harvey; Durbin & Koopman (state space treatment); Kalman filter	Box & Jenkins (Box-Jenkins methodology)	Hamilton (1989); Kim & Nelson (1999)
유형	State space time series model	Univariate time-series model	Regime-switching time series model
원전	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.	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	Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384.
별칭	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR
관련	4	5	5
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.	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).	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.