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Forecast VAR Model Using Monte Carlo Simulation

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This example shows how to use Monte Carlo simulation via simulate to forecast a VAR model.

simulate enables you to generate simulations of time series based on your model. If you have a trustworthy VAR model object, you can use these simulations as sample forecasts.

simulate requires:

  • A model (EstMdl in what follows)

  • The number of periods for the forecast (numobs in what follows)

simulate optionally takes:

  • An exogenous data series

  • A presample time series (Y(end-3:end,:) in what follows)

  • Future sample responses for conditional simulation

  • The number of realizations, or paths, to simulate (2000 in what follows)

Load the Data_USEconModel data set. This example uses two time series: the logarithm of real GDP, and the real 3-month T-bill rate, both differenced to be approximately stationary. For illustration, a VAR(4) model describes the time series.

load Data_USEconModel
DEF = log(DataTimeTable.CPIAUCSL);
GDP = log(DataTimeTable.GDP);
rGDP = diff(GDP - DEF); % Real GDP is GDP - deflation
TB3 = 0.01*DataTimeTable.TB3MS;
dDEF = 4*diff(DEF); % Scaling
rTB3 = TB3(2:end) - dDEF; % Real interest is deflated
Y = [rGDP,rTB3];

Fit a VAR(4) model specification.

Mdl = varm(2,4);
Mdl.SeriesNames = {'Transformed real GDP','Transformed real 3-mo T-bill rate'};
EstMdl = estimate(Mdl,Y);

Define the forecast horizon.

numobs = 21;
FDates = dateshift(DataTimeTable.Time(end),'end','quarter',1:numobs);

Simulate the model for numobs steps ahead, and generate 2000 paths. Specify presample observations from the end of the data.

rng(1); %For reproducibility
Ysim = simulate(EstMdl,numobs,'Y0',Y(end-3:end,:),'NumPaths',2000);

Calculate the mean and standard deviation of the simulated series:

Ymean = mean(Ysim,3); % Calculate means
Ystd = std(Ysim,0,3); % Calculate std deviations

Plot the means +/- 1 standard deviation for the simulated series:

figure;
subplot(2,1,1)
plot(DataTimeTable.Time(end-10:end),Y(end-10:end,1),'k')
hold('on')
plot([DataTimeTable.Time(end) FDates],[Y(end,1);Ymean(:,1)],'r')
plot([DataTimeTable.Time(end) FDates],[Y(end,1);Ymean(:,1)]+[0;Ystd(:,1)],'b')
plot([DataTimeTable.Time(end) FDates],[Y(end,1);Ymean(:,1)]-[0;Ystd(:,1)],'b')
title('Transformed Real GDP')
subplot(2,1,2)
plot(DataTimeTable.Time(end-10:end),Y(end-10:end,2),'k')
hold('on')
plot([DataTimeTable.Time(end) FDates],[Y(end,2);Ymean(:,2)],'r')
plot([DataTimeTable.Time(end) FDates],[Y(end,2);Ymean(:,2)]+[0;Ystd(:,2)],'b')
plot([DataTimeTable.Time(end) FDates],[Y(end,2);Ymean(:,2)]-[0;Ystd(:,2)],'b')
title('Transformed Real 3-mo T-bill Rate')

Figure contains 2 axes objects. Axes object 1 with title Transformed Real GDP contains 4 objects of type line. Axes object 2 with title Transformed Real 3-mo T-bill Rate contains 4 objects of type line.

See Also

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