Diffuse State-Space Model

States that can have infinite initial variances

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
forecastForecast states and observations of diffuse state-space models

Examples and How To

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.

Concepts

What Are State-Space Models?

State-space model definition and usages

What Is the Kalman Filter?

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

Rolling-Window Analysis of Time-Series Models

Estimate explicitly and implicitly defined state-space models using a rolling window.