How to present a model which is based on the result not yet discussed?
You have data S, and a model M={H, f(H), g(H)}, where H are the set of assumptions of the model, and {f(H), g(H)} are components of the model that of course depend on the assumptions in some way. 

Using the data, you estimate a functional form for f(H), for lack of mathjax denote it empf(S,H)

Then you apply your model to the data and obtain a measure of fit, expressed here in abstract as a distance measure between model and data

{M,S}->d(M,S)*   or **{[H, empf(S,H), g(H)], S}->d([H, empf(S,H), g(H)],S)  

One (not all) components of your model had a chance to "fit with the data"  prior to applying the whole model, and so it has increased the chances of your model as a whole to fit the data. 

So the "distance" is not purely between model and data -it is a distance between a model that has already come "closer to the data", through empf(S,H), and the data.  

In a rather known dialect, "you trained (partly) your model, using the same data set on which you are going to test it". I would not consider this as scientifically acceptable. 

The way out of course, is to estimate f by using a different data set, a data set that should, according to your theory, be able also to be represented by the same model. And this also guides you as to how you could go about writing your paper.