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Assuming that <math>\mathcal{T}_X = \mathcal{T}_Y := \mathcal{T}</math>, another model called varying-coefficient model is of the form
<math display="block">Y(s) = \alpha_0(s) + \alpha(s)X(s)+\epsilon(s)</math>
Note that this model assumes the value of <math>Y</math> at time <math>s</math>, i.e. <math>Y(s)</math>, only depends on that of <math>X</math> at the same time, <math>X(s)</math>, and thus is a concurrent regression model. For estimation, one may use the fact that, for any <math>s\in\mathcal{T}</math> fixed, an estimate of <math>\alpha(s)</math> can be computed by applying [[Ordinary least squares|ordinary least squares]] to a neighborhood of <math>s</math><ref name=wang:16/>.
== Functional nonlinear models ==
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