Main Content

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 and to learn how to create a model in MATLAB®, see Create Continuous State-Space Models for Economic Data Analysis.

Functions

expand all

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

Topics

Create Model

Fit Model to Data

Estimate State Variables

Characterize Dynamic Behavior

Generate Monte Carlo Simulations

Generate Minimum Mean Square Error Forecasts