treatment of time derivatives. Since the goal is to obtain an estimate of a CT system, knowledge of the derivatives of the input and output are, either explicitly or implicitly, required. However, these derivatives are not exactly computable when only sampled input-output data is obtained. To overcome this problem, many algorithms have been suggested (see, e.g., [@sinha1991identification; @rao2006identification] and the references therein). One of the most popular algorithms is the Simplified Refined Instrumental Variable method for Continuous-time systems (SRIVC), which was first presented in [@young1980refined]. This method has been suggested for general use due to its robustness and accuracy in practical applications [@garnier2014advantages]. Many further extensions of this method also exist in the literature, for example, to handle non-uniformly sampled data [@huselstein2002approach] or multi-input systems [@garnier2007optimal]. Extensions to output error (OE) and Box-Jenkins (BJ) models [@chen2013refined], unification of DT and CT transfer function estimation [@young2015refined], and a comprehensive consistency analysis [@pan2020consistency] have also been presented.

The SRIVC algorithm uses interpolation of the input and output data in order to compute filtered regressor and instrument vectors in an iterative estimation procedure. This reconstruction of the CT input and output signals is usually implemented through simple interpolation schemes like zero-order hold (ZOH) 