Issues in generating AR model with a constraint I am new to the topic of system identification and looking for a large Autoregressive (AR) model. Can anybody point out a large stable AR model which has more than 2 coefficients AND there should be a large number of zero coefficients? 

This link presents an example of how to have nonzero coefficients in lag 1,4 for order, $p=4$ AR(4) model. But, the values are NaN !! So, how do I apply this technique so as to generate a large AR model with more number of zero coefficients and lesser number of non-zero coefficients ? 

EDIT:

Looking for any stable AR($p$) model (preferably popular cited ones, if available) of higher order $p \ge 20$ where the number of non-zero coefficients are few (constraint) . If I generate a noisy sinusoidal wave given below, then 

%Generate sine wave = A*sin(2*pi*f*t + phi)
t = linspace(0,1,1000);
A = 5;
f = 2;
phi = pi/8;
sinewave = A*sin(2*pi*f*t + phi);
noisy_sine=sinewave+0.5*randn(size(t));
subplot(1,2,1);
plot(t, sinewave)
hold on;
subplot(1,2,2);
plot(t,noisy_sine);

%Generate AR model
order =20;
ARCoeff = aryule(noisy_sine,order);


It returns AR model of 20 lags. But, all the coefficients are non-zero. How do I generate AR model of any order higher than 2 where the number of non-zero coefficients are lesser than the number of zero coefficients ?
I was able to produce the AR(p) model with much of zero-coefficients. I use Scilab rather than Matlab, therefore I'll just describe the technique:


Generate some appropriate data with the desired spectral properties - the AR model will mimic these spectral properties.
Fit the AR model of the desired order to your data.
Find the desired number of the coefficients with the lowest values and reset them to zero  (something like a(find(abs(a) &lt; stdev(a) / 2)) = 0 - valid in Scilab).


The resulting model should (may?) still has a similar spectral properties &amp; satisfy your requirements considering zero-coefficients.