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.

Implicitly Create State-Space Model Containing Regression Component

This example shows how to implicitly create a state-space model that contains a regression component in the observation equation. The state model is an ARMA(1,1).

Write a function that specifies how the parameters in params map to the state-space model matrices, the initial state values, and the type of state. Specify the regression component by deflating the observations within the function. Symbolically, the model is:


% Copyright 2015 The MathWorks, Inc.

function [A,B,C,D,Mean0,Cov0,StateType,DeflateY] = regressionParamMap(params,y,z)
% State-space model with a regression component parameter mapping function
% example. This function maps the vector params to the state-space matrices
% (A, B, C, and D), the initial state value and the initial state variance
% (Mean0 and Cov0), and the type of state (StateType). The state model is
% an ARMA(1,1).
    varu1 = exp(params(3)); % Positive variance constraint
    vare1 = exp(params(4));
    A = [params(1) params(2); 0 0];
    B = [sqrt(varu1); 1]; 
    C = [1 0];
    D = sqrt(vare1);
    Mean0 = [0.5 0.5];
    Cov0 = eye(2);
    StateType = [0 0];
    DeflateY = y - params(5)*z;
end

Save this code as a file named regressionParamMap on your MATLAB® path.

Create the state-space model by passing the function regressionParamMap as a function handle to ssm.

Mdl = ssm(@(params)regressionParamMap(params,y,z));

ssm implicitly creates the state-space model. Usually, you cannot verify implicitly defined state-space models.

Before creating the model, ensure that the data y and z exist in your workspace.

See Also

| |

Related Examples

More About