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	Model przestrzeni stanów (filtr Kalmana)	Model ARIMA (Autoregressive Integrated Moving Average)	Autoregresja wektorowa bayesowska (BVAR)	Model strukturalny szeregów czasowych (Podstawowy model strukturalny)
Dziedzina	Ekonometria	Ekonometria	Ekonometria	Ekonometria
Rodzina	Regression model	Regression model	Regression model	Regression model
Rok powstania	1990	2015	1986	1990
Twórca	Harvey; Durbin & Koopman (state space treatment); Kalman filter	Box & Jenkins (Box-Jenkins methodology)	Litterman (1986); Bańbura, Giannone & Reichlin (2010)	Andrew C. Harvey
Typ	State space time series model	Univariate time-series model	Bayesian multivariate time-series model	State-space (unobserved components) time series model
Źródło pierwotne	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	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
Inne nazwy	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR)	BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM)
Pokrewne	4	5	5	4
Podsumowanie	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).	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.