手法を比較

	ベイズ型ベクトル自己回帰（BVAR）	状態空間モデル（カルマンフィルタ）
分野	計量経済学	計量経済学
系統	Regression model	Regression model
提唱年	1986	1990
提唱者	Litterman (1986); Bańbura, Giannone & Reichlin (2010)	Harvey; Durbin & Koopman (state space treatment); Kalman filter
種類	Bayesian multivariate time-series model	State space time series model
原典	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.
別名	BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR)	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
関連	5	4
概要	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.	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.