This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English version of the page.

Note: This page has been translated by MathWorks. Click here to see
To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

State-Space Models

Continuous state-space processes characterized by state and observation equations

State-space models specify the structure of unobserved dynamic processes, and the composition of the processes into observations. Econometrics Toolbox™ state-space functionality accommodates time-invariant or time-varying linear state-space models containing mean-zero Gaussian state disturbances and observation innovations. The initial state distributions can be stationary, constant, or diffuse.

You can create a standard or diffuse state-space model using ssm or dssm, respectively. After creating a state-space model, you can estimate any unknown parameters using time-series data, obtain filtered states, smooth states, or generate forecasts. To filter and smooth states, Econometrics Toolbox implements the standard or diffuse Kalman filter.

Featured Examples