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This example shows how to simulate trend-stationary and difference-stationary processes. The simulation results illustrate the distinction between these two nonstationary process models.

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Step 1. Generate realizations from a trend-stationary process. Step 2. Generate realizations from a difference-stationary process. |

**Step 1. Generate realizations from a trend-stationary process.**

Specify the trend-stationary process

where the innovation process is Gaussian with variance 8. After specifying the model, simulate 50 sample paths of length 200. Use 100 burn-in simulations.

t = [1:200]'; trend = 0.5*t; model = arima('Constant',0,'MA',{1.4,0.8},'Variance',8); rng('default') u = simulate(model,300,'NumPaths',50); Yt = repmat(trend,1,50) + u(101:300,:); figure plot(Yt,'Color',[.85,.85,.85]) hold on h1=plot(t,trend,'r','LineWidth',5); xlim([0,200]) title('Trend-Stationary Process') h2=plot(mean(Yt,2),'k--','LineWidth',2); legend([h1,h2],'Trend','Simulation Mean',... 'Location','NorthWest') hold off

The sample paths fluctuate around the theoretical trend line with constant variance. The simulation mean is close to the true trend line.

**Step 2. Generate realizations from a difference-stationary process.**

Specify the difference-stationary model

where the innovation distribution is Gaussian with variance 8. After specifying the model, simulate 50 sample paths of length 200. No burn-in is needed because all sample paths should begin at zero. This is the `simulate` default starting point for nonstationary processes with no presample data.

model = arima('Constant',0.5,'D',1,'MA',{1.4,0.8},... 'Variance',8); rng('default') Yd = simulate(model,200,'NumPaths',50); figure plot(Yd,'Color',[.85,.85,.85]) hold on h1=plot(t,trend,'r','LineWidth',5); xlim([0,200]) title('Difference-Stationary Process') h2=plot(mean(Yd,2),'k--','LineWidth',2); legend([h1,h2],'Trend','Simulation Mean',... 'Location','NorthWest') hold off

The simulation average is close to the trend line with slope 0.5. The variance of the sample paths grows over time.

**Step 3. Difference the sample paths.**

A difference-stationary process is stationary when differenced appropriately. Take the first differences of the sample paths from the difference-stationary process, and plot the differenced series. One observation is lost as a result of the differencing.

diffY = diff(Yd,1,1); figure plot(2:200,diffY,'Color',[.85,.85,.85]) xlim([0,200]) title('Differenced Series') hold on h = plot(2:200,mean(diffY,2),'k--','LineWidth',2); legend(h,'Simulation Mean','Location','NorthWest') hold off

The differenced series looks stationary, with the simulation mean fluctuating around zero.

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