%% Specify More Lags for the PACF Plot
% Specify the multiplicative seasonal ARMA (2,0,1)\times(3,0,0)_{12} model:
% (1-0.75L-0.15L^2)(1-0.9L^{12}+0.75L^{24}-0.5L^{36})y_t = 2+\varepsilon_t-0.5\varepsilon_{t-1},
% where \varepsilon_t is Gaussian with mean 0 and variance 1.

Mdl = arima('AR',{0.75,0.15},'SAR',{0.9,-0.75,0.5},...
    'SARLags',[12,24,36],'MA',-0.5,'Constant',2,...
    'Variance',1);

%
% Simulate data from Mdl.

rng(1);
y = simulate(Mdl,1000); 

%
% Plot the default partial autocorrelation function (PACF).

figure
parcorr(y)

% The default correlogram does not display the dependence structure for higher lags.
%
% Plot the PACF for 40 lags.

figure
parcorr(y,'NumLags',40)

% The correlogram shows the larger correlations at lags 12, 24, and 36.
% Copyright 2015 The MathWorks, Inc.