Create State-Space Model with Unknown Parameters
Explicitly Create State-Space Model Containing Unknown Parameters
This example shows how to create a time-invariant, state-space model containing unknown parameter values using
Define a state-space model containing two dependent MA(1) states, and an additive-error observation model. Symbolically, the equation is
Note that the states and are the two dependent MA(1) processes. The states and help construct the lag-one, MA effects. For example, picks up the first disturbance (), and picks up . In all, , which is an MA(1) with as an input.
Specify the state-transition coefficient matrix. Use
NaN values to indicate unknown parameters.
A = [0 NaN NaN 0; 0 0 0 0; 0 0 0 NaN; 0 0 0 0];
Specify the state-disturbance-loading coefficient matrix.
B = [NaN 0; 1 0; 0 NaN; 0 1];
Specify the measurement-sensitivity coefficient matrix.
C = [1 0 0 0; 0 0 1 0];
Specify the observation-innovation coefficient matrix.
D = [NaN 0; 0 NaN];
ssm to define the state-space model.
Mdl = ssm(A,B,C,D)
Mdl = State-space model type: ssm State vector length: 4 Observation vector length: 2 State disturbance vector length: 2 Observation innovation vector length: 2 Sample size supported by model: Unlimited Unknown parameters for estimation: 7 State variables: x1, x2,... State disturbances: u1, u2,... Observation series: y1, y2,... Observation innovations: e1, e2,... Unknown parameters: c1, c2,... State equations: x1(t) = (c1)x2(t-1) + (c2)x3(t-1) + (c4)u1(t) x2(t) = u1(t) x3(t) = (c3)x4(t-1) + (c5)u2(t) x4(t) = u2(t) Observation equations: y1(t) = x1(t) + (c6)e1(t) y2(t) = x3(t) + (c7)e2(t) Initial state distribution: Initial state means are not specified. Initial state covariance matrix is not specified. State types are not specified.
Mdl is an
ssm model containing unknown parameters. A detailed summary of
Mdl prints to the Command Window. It is good practice to verify that the state and observations equations are correct.
Mdl and data to
estimate to estimate the unknown parameters.
Implicitly Create Time-Invariant State-Space Model
This example shows how to create a time-invariant state-space model by passing a parameter-mapping function describing the model to
ssm (that is, implicitly create a state-space model). The state model is AR(1) model. The states are observed with bias, but without random error. Set the initial state mean and variance, and specify that the state is stationary.
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.
% Copyright 2015 The MathWorks, Inc. function [A,B,C,D,Mean0,Cov0,StateType] = timeInvariantParamMap(params) % Time-invariant state-space model 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 AR(1) % without observation error. varu1 = exp(params(2)); % Positive variance constraint A = params(1); B = sqrt(varu1); C = params(3); D = ; Mean0 = 0.5; Cov0 = 100; StateType = 0; end
Save this code as a file named
timeInvariantParamMap to a folder on your MATLAB® path.
Create the state-space model by passing the function
timeInvariantParamMap as a function handle to
Mdl = ssm(@timeInvariantParamMap);
The software implicitly defines the state-space model. Usually, you cannot verify state-space models that you implicitly define.
Mdl is an
ssm model object containing unknown parameters. You can estimate the unknown parameters by passing
Mdl and response data to
- Explicitly Create State-Space Model Containing Known Parameter Values
- Create State-Space Model Containing ARMA State
- Implicitly Create Time-Varying State-Space Model
- Create State-Space Model with Random State Coefficient