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Dynamical systems describe the changes in processes that arise naturally from their underlying physical principles, such as the laws of motion or the conservation of mass, energy or momentum. These models facilitate a causal explanation for the drivers and impediments of the processes. But do they describe the behaviour of the observed data? And how can we quantify the models' parameters that cannot be measured directly? This paper addresses these two questions by providing a methodology for estimating the solution; and the parameters of linear dynamical systems from incomplete and noisy observations of the processes. The proposed procedure builds on the parameter cascading approach, where a linear combination of basis functions approximates the implicitly defined solution of the dynamical system. The systems' parameters are then estimated so that this approximating solution adheres to the data. By taking advantage of the linearity of the system, we have simplified the parameter cascading estimation procedure, and by developing a new iterative scheme, we achieve fast and stable computation. We illustrate our approach by obtaining a linear differential equation that represents real data from biomechanics. Comparing our approach with popular methods for estimating the parameters of linear dynamical systems, namely, the non-linear least-squares approach, simulated annealing, parameter cascading and smooth functional tempering reveals a considerable reduction in computation and an improved bias and sampling variance.