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sys = ivar(data,na)
sys = ivar(data,na,nc)
sys = ivar(data,na,nc,max_size)
sys = ivar(data,na) estimates an AR polynomial model, sys, using the instrumental variable method and the time series data data. na specifies the order of the A polynomial.
An AR model is represented by the equation:
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In the above model, e(t) is an arbitrary process, assumed to be a moving average process of order nc, possibly time varying. nc is assumed to be equal to na. Instruments are chosen as appropriately filtered outputs, delayed nc steps.
sys = ivar(data,na,nc) specifies the value of the moving average process order, nc, separately.
sys = ivar(data,na,nc,max_size) specifies the maximum size of matrices formed during estimation.
sys |
Identified polynomial model. sys is an AR idpoly model which encapsulates the identified polynomial model. |
Compare spectra for sinusoids in noise, estimated by the IV method and by the forward-backward least squares method.
y = iddata(sin([1:500]'*1.2) + sin([1:500]'*1.5) + ...
0.2*randn(500,1),[]);
miv = ivar(y,4);
mls = ar(y,4);
spectrum(miv,mls)
[1] Stoica, P., et al. Optimal Instrumental Variable Estimates of the AR-parameters of an ARMA Process, IEEE Trans. Autom. Control, Volume AC-30, 1985, pp. 1066–1074.
ar | arx | etfe | idpoly | polyest | spa | spectrum | step

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