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Diffuse State-Space Model

States can have infinite initial variances

The diffuse state-space model implements the diffuse Kalman filter and initial state variances of infinite. You can create a diffuse state-space model by calling dssm.

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

Functions

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dssmCreate diffuse state-space model
estimateMaximum likelihood parameter estimation of diffuse state-space models
refineRefine initial parameters to aid diffuse state-space model estimation
dispDisplay summary information for diffuse state-space model
filterForward recursion of diffuse state-space models
smoothBackward recursion of diffuse state-space models
irfImpulse response function (IRF) of state-space model
irfplotPlot impulse response function (IRF) of state-space model
fevdGenerate forecast error variance decomposition (FEVD) of state-space model
forecastForecast states and observations of diffuse state-space models

Topics

What Are State-Space Models?

Learn state-space model definitions and how to create a state-space model object.

What Is the Kalman Filter?

Learn about the Kalman filter, and associated definitions and notations.

Implicitly Create Time-Varying Diffuse State-Space Model

Create a diffuse state-space model in which one of the state variables drops out of the model after a certain period.

Implicitly Create Diffuse State-Space Model Containing Regression Component

Create a diffuse state-space model that contains a regression component in the observation equation using a parameter-mapping function describing the model.

Estimate Time-Varying Diffuse State-Space Model

Fit diffuse state-space model to data.

Filter Time-Varying Diffuse State-Space Model

Generate data from a known model, fit a diffuse state-space model to the data, and then filter the states.

Smooth Time-Varying Diffuse State-Space Model

Generate data from a known model, fit a diffuse state-space model to the data, and then smooth the states.

Forecast Time-Varying Diffuse State-Space Model

Generate data from a known model, fit a diffuse state-space model to the data, and then forecast states and observations states from the fitted model.