# Documentation

### This is machine translation

Translated by
Mouseover text to see original. Click the button below to return to the English verison of the page.

## Create State-Space Model Containing ARMA State

This example shows how to create a stationary ARMA model subject to measurement error using `ssm`.

To explicitly create a state-space model, it is helpful to write the state and observation equations in matrix form. In this example, the state of interest is the ARMA(2,1) process

where is Gaussian with mean 0 and known standard deviation 0.5.

The variables , , and are in the state-space model framework. Therefore, the terms , , and require "dummy states" to be included in the model.

Subsequently, the state equation is

Note that:

• c corresponds to a state () that is always `1`.

• , and has the term .

• has the term . `ssm` puts state disturbances as Gaussian random variables with mean 0 and variance 1. Therefore, the factor `0.5` is the standard deviation of the state disturbance.

• , and has the term .

The observation equation is unbiased for the ARMA(2,1) state process. The observation innovations are Gaussian with mean 0 and known standard deviation 0.1. Symbolically, the observation equation is

You can include a measurement-sensitivity factor (a bias) by replacing `1` in the row vector by a scalar or unknown parameter.

Define the state-transition coefficient matrix. Use `NaN` values to indicate unknown parameters.

```A = [NaN NaN NaN NaN; 0 1 0 0; 1 0 0 0; 0 0 0 0]; ```

```B = [0.5; 0; 0; 1]; ```

Define the measurement-sensitivity coefficient matrix.

```C = [1 0 0 0]; ```

Define the observation-innovation coefficient matrix.

```D = 0.1; ```

Use `ssm` to create the state-space model. Set the initial-state mean (`Mean0`) to a vector of zeros and covariance matrix (`Cov0`) to the identity matrix, except set the mean and variance of the constant state to `1` and `0`, respectively. Specify the type of initial state distributions (`StateType`) by noting that:

• is a stationary, ARMA(2,1) process.

• is the constant 1 for all periods.

• is the lagged ARMA process, so it is stationary.

• is a white-noise process, so it is stationary.

```Mean0 = [0; 1; 0; 0]; Cov0 = eye(4); Cov0(2,2) = 0; StateType = [0; 1; 0; 0]; Mdl = ssm(A,B,C,D,'Mean0',Mean0,'Cov0',Cov0,'StateType',StateType); ```

`Mdl` is an `ssm` model. You can use dot notation to access its properties. For example, print `A` by entering `Mdl.A`.

Use `disp` to verify the state-space model.

```disp(Mdl) ```
```State-space model type: <a href="matlab: doc ssm">ssm</a> State vector length: 4 Observation vector length: 1 State disturbance vector length: 1 Observation innovation vector length: 1 Sample size supported by model: Unlimited Unknown parameters for estimation: 4 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)x1(t-1) + (c2)x2(t-1) + (c3)x3(t-1) + (c4)x4(t-1) + (0.50)u1(t) x2(t) = x2(t-1) x3(t) = x1(t-1) x4(t) = u1(t) Observation equation: y1(t) = x1(t) + (0.10)e1(t) Initial state distribution: Initial state means x1 x2 x3 x4 0 1 0 0 Initial state covariance matrix x1 x2 x3 x4 x1 1 0 0 0 x2 0 0 0 0 x3 0 0 1 0 x4 0 0 0 1 State types x1 x2 x3 x4 Stationary Constant Stationary Stationary ```

If you have a set of responses, you can pass them and `Mdl` to `estimate` to estimate the parameters.