Diffuse State-Space Model

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

Functions

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Create Model

dssm Create diffuse linear Gaussian state-space model

Fit Model to Data

`estimate`	Maximum likelihood parameter estimation of diffuse state-space models
`refine`	Refine initial parameters to aid diffuse state-space model estimation
`disp`	Display summary information for diffuse state-space model

Estimate State Variables

`filter`	Forward recursion of diffuse state-space models
`smooth`	Backward recursion of diffuse state-space models

Characterize Dynamic Behavior

`irf`	Impulse response function (IRF) of state-space model (Since R2020b)
`irfplot`	Plot impulse response function (IRF) of state-space model (Since R2020b)
`fevd`	Generate forecast error variance decomposition (FEVD) of state-space model (Since R2021a)

Generate Minimum Mean Square Error Forecasts

forecast Forecast states and observations of diffuse state-space models

Topics

Create Continuous State-Space Models for Economic Data Analysis
Learn how Econometrics Toolbox™ supports state-space modeling of time series.
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.