Compara mètodes

	Model d'espai d'estats (Filtre de Kalman)	Model d'ARIMA (Autoregressive Integrated Moving Average)	Vector Autoregressiu Bayesà (BVAR)	Model de commutació de règims de Markov (MS-AR / MS-VAR)
Camp	Econometria	Econometria	Econometria	Econometria
Família	Regression model	Regression model	Regression model	Regression model
Any d'origen	1990	2015	1986	1989
Autor original	Harvey; Durbin & Koopman (state space treatment); Kalman filter	Box & Jenkins (Box-Jenkins methodology)	Litterman (1986); Bańbura, Giannone & Reichlin (2010)	Hamilton (1989); Kim & Nelson (1999)
Tipus	State space time series model	Univariate time-series model	Bayesian multivariate time-series model	Regime-switching time series model
Font seminal	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.	Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384.
Àlies	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)	regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR
Relacionats	4	5	5	5
Resum	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 Markov regime-switching model lets the parameters of a time series change probabilistically across hidden regimes governed by a Markov chain. Introduced by Hamilton (1989) and developed further by Kim and Nelson (1999), it automatically detects business-cycle phases such as expansions and contractions.