method; Consistency

Introduction
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System identification involves using measured input and output data for building mathematical models that characterise a system’s behaviour. Different approaches to system identification have been developed depending on whether a discrete-time (DT) or continuous-time (CT) model is needed. Continuous-time system identification has applications in many areas of science and engineering such as economics, biology, physics and control, with comprehensive literature written on the subject [@rao2006identification; @Garnier2008book; @young2012recursive]. Although the system identification community has focused mainly in DT setups, as it has been investigated during a predominantly digital era, there are many reasons why CT system identification has had a renewed interest during the last decades [@garnier2015direct]. For example, model coefficients are directly linked to physical parameters, and more parsimonious models can be obtained as knowledge of the relative degree of the CT system can be accommodated. Also, contrary to the DT system identification using the forward shift operator, irregular and fast sampling can be easily handled, since the associated parameters remain invariant with respect to the varying sampling period and the model poles do not become statistically ill-defined as the sampling period decays to zero.

One of the main difficulties in CT system identification is the 