Regression model
State Space Model (Kalman Filter)
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.
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Sources
- Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI: 10.1017/CBO9781107049994 ↗
- Durbin, J. & Koopman, S. J. (2012). Time Series Analysis by State Space Methods (2nd ed.). Oxford University Press. DOI: 10.1093/acprof:oso/9780199641178.001.0001 ↗
Related methods
Referenced by
Bayesian SARIMA ModelBayesian Structural Time SeriesDigital Twin SimulationDSGE ModelEnsemble Kalman FilterETS ModelExponential SmoothingFiLMHolt-WintersHP FilterKalman Filter with Missing DataKoopaParticle FilterProphetRobust ARIMA modelSARIMASARIMAXTime-varying parameter AR modelTime-varying parameter ARIMA modelTime-varying parameter ARMA modelTime-varying parameter dynamic panel data modelTime-varying parameter Engle-Granger cointegrationTime-varying parameter fixed effects modelTime-varying parameter GARCH modelTime-varying parameter GLSTime-varying parameter Hausman testTime-varying parameter OLSTime-varying Parameter Panel Data AnalysisTime-varying parameter SARIMA modelTime-varying parameter TGARCH modelTime-varying parameter VAR modelTime-varying parameter VECMTime-varying parameter WLS