विधियों की तुलना करें

	स्टेट स्पेस मॉडल (कलमन फिल्टर)	बायेसियन वेक्टर ऑटोरिग्रेशन (BVAR)
क्षेत्र	अर्थमिति	अर्थमिति
परिवार	Regression model	Regression model
उद्भव वर्ष	1990	1986
प्रवर्तक	Harvey; Durbin & Koopman (state space treatment); Kalman filter	Litterman (1986); Bańbura, Giannone & Reichlin (2010)
प्रकार	State space time series model	Bayesian multivariate time-series model
मौलिक स्रोत	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.
उपनाम	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)	BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR)
संबंधित	4	5
सारांश	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.