in MATLAB.

**.** The precision of this procedure will depend on the over-sampling period $\delta h$. Via extensive simulations, we have found that sampling at least 100 times faster is usually enough to provide reliable estimates.

Simulation examples {#sec5}
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Via numerical simulations we compare the performance of the standard SRIVC method with the proposed SRIVC-type method, which is labeled SRIVC-c. For a multisine input, we examine the consistency of both methods for different regular sampling period and also for irregular sampling. We also study the consistency of each method under an arbitrary input excitation for different regular sampling periods. For the following tests, we consider the system $$\label{systemexample}
G^*(p) = \frac{1.25}{0.25p^2+0.7p+1},$$ where the parameters of interest are $a_1^*=0.25$, , and $b_0^*=1.25$. Regarding the implementation of the standard SRIVC method, we have used the `srivc` command from the CONTSID toolbox version 7.3 for MATLAB [@garnier2018contsid], under default initialisation and tolerance settings. It was set to estimate the best model among the correct model structure with a FOH as the intersample behaviour.

Multisine input: Regular sampling
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We first test if the algorithms provide consistent estimates of the parameter vector $[a_1, \hspace{0.1cm} a_2, \hspace{0.10cm} b_0]^\top$. The system in is excited with 