手法を比較

	状態空間モデル（カルマンフィルタ）	ベイズ型ベクトル自己回帰（BVAR）	構造的時系列モデル（基本構造モデル）
分野	計量経済学	計量経済学	計量経済学
系統	Regression model	Regression model	Regression model
提唱年	1990	1986	1990
提唱者	Harvey; Durbin & Koopman (state space treatment); Kalman filter	Litterman (1986); Bańbura, Giannone & Reichlin (2010)	Andrew C. Harvey
種類	State space time series model	Bayesian multivariate time-series model	State-space (unobserved components) time series model
原典	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.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737
別名	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)	BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR)	BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM)
関連	4	5	4
概要	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 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.