## Simulate States of Time-Varying State-Space Model Using Simulation Smoother

This example generates data from a known model, fits a state-space model to the data, and then simulates series from the fitted model using the simulation smoother.

Suppose that a latent process comprises an AR(2) and an MA(1) model. There are 50 periods, and the MA(1) process drops out of the model for the final 25 periods. Subsequently, the state equation for the first 25 periods is

` `

and for the last 25 periods, it is

` `

where and are Gaussian with mean 0 and standard deviation 1.

Assuming that the series starts at 1.5 and 1, respectively, generate a random series of 50 observations from and .

```T = 50; ARMdl = arima('AR',{0.7,-0.2},'Constant',0,'Variance',1); MAMdl = arima('MA',0.6,'Constant',0,'Variance',1); x0 = [1.5 1; 1.5 1]; rng(1); x = [simulate(ARMdl,T,'Y0',x0(:,1)),... [simulate(MAMdl,T/2,'Y0',x0(:,2));nan(T/2,1)]]; ```

The last 25 values for the simulated MA(1) data are `NaN` values.

Suppose further that the latent processes are measured using

` `

for the first 25 periods, and

` `

for the last 25 periods, where is Gaussian with mean 0 and standard deviation 1.

Use the random latent state process (`x`) and the observation equation to generate observations.

```y = 2*nansum(x')'+randn(T,1); ```

Together, the latent process and observation equations compose a state-space model. Supposing that the coefficients are unknown parameters, the state-space model is

` `

for the first 25 periods,

` `

for period 26, and

` `

for the last 24 periods.

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] = AR2MAParamMap(params,T) %AR2MAParamMap Time-variant state-space model parameter mapping function % % 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). From periods 1 to T/2, the % state model is an AR(2) and an MA(1) model, and the observation model is % the sum of the two states. From periods T/2 + 1 to T, the state model is % just the AR(2) model. A1 = {[params(1) params(2) 0 0; 1 0 0 0; 0 0 0 params(3); 0 0 0 0]}; B1 = {[1 0; 0 0; 0 1; 0 1]}; C1 = {params(4)*[1 0 1 0]}; Mean0 = ones(4,1); Cov0 = 10*eye(4); StateType = [0 0 0 0]; A2 = {[params(1) params(2) 0 0; 1 0 0 0]}; B2 = {[1; 0]}; A3 = {[params(1) params(2); 1 0]}; B3 = {[1; 0]}; C3 = {params(5)*[1 0]}; A = [repmat(A1,T/2,1);A2;repmat(A3,(T-2)/2,1)]; B = [repmat(B1,T/2,1);B2;repmat(B3,(T-2)/2,1)]; C = [repmat(C1,T/2,1);repmat(C3,T/2,1)]; D = 1; end ```

Save this code as a file named `AR2MAParamMap` on your MATLAB® path.

Create the state-space model by passing the function `AR2MAParamMap` as a function handle to `ssm`.

```Mdl = ssm(@(params)AR2MAParamMap(params,T)); ```

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

Simulate one path of states from Mdl using the simulation smoother. Specify that the parameter-to-matrix mapping function has seven output arguments. Also, specify the unknown values of the parameters.

```simParams = [0.48 0.0081 0.56 1.63 1.9]; X = simsmooth(Mdl,y,'NumOut',7,'Params',simParams); ```

`X` is a `T`-by-1 cell vector of simulated states. Cells 1 through 25 contain 4-by-1 vectors, and cells 26 through 50 contain 2-by-1 vectors.

Access a cell using cell indexing, for example, access cell 5 using `X{5}`.

```simStatesPeriod5 = X{5} ```
```simStatesPeriod5 = -1.7591 -1.5404 -1.5171 -1.1417 ```