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Standard State-Space Model

States with finite initial state variances

The standard state-space model implements the standard Kalman filter and initial state variances of are finite. You can create a standard state-space model by calling ssm.

For an overview of supported state-space model forms, see What Are State-Space Models?.


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ssmCreate standard linear Gaussian state-space model
estimateMaximum likelihood parameter estimation of state-space models
refineRefine initial parameters to aid state-space model estimation
dispDisplay summary information for state-space model
filterForward recursion of state-space models
smoothBackward recursion of state-space models
updateReal-time state update by state-space model Kalman filtering (Since R2021b)
irfImpulse response function (IRF) of state-space model (Since R2020b)
irfplotPlot impulse response function (IRF) of state-space model (Since R2020b)
fevdGenerate forecast error variance decomposition (FEVD) of state-space model (Since R2021a)
corrModel-implied temporal correlations of state-space model (Since R2021a)
simulateMonte Carlo simulation of state-space models
simsmoothState-space model simulation smoother
forecastForecast states and observations of state-space models


Create Model

Fit Model to Data

Estimate State Variables

Characterize Dynamic Behavior

Generate Monte Carlo Simulations

Generate Minimum Mean Square Error Forecasts