Σύγκριση μεθόδων

	Μοντέλο Χώρου Καταστάσεων (Φίλτρο Kalman)	Διανυσματική Αυτοπαλίνδρομη Συσχέτιση (BVAR) με Μπεϋζιανή Προσέγγιση
Πεδίο	Οικονομετρία	Οικονομετρία
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	1990	1986
Δημιουργός	Harvey; Durbin & Koopman (state space treatment); Kalman filter	Litterman (1986); Bańbura, Giannone & Reichlin (2010)
Τύπος	State space time series model	Bayesian multivariate time-series model
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)	BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR)
Συναφείς	4	5
Σύνοψη	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.