When estimating a basic macroeconomics model, why is goodness of fit with labor data not often dealt with? So when dealing with not-too-complex RBC models or New Keynesian models, we often arrive with a few final reduced-form equations, and as far as I know, we use these equations to estimate the model. For Gali's simple New Keynesian model we have consumption Euler equation, New Keynesian Phillips Curve and some form of Taylor rule. 

But it seems that when estimating them, labor data (for example, labor $N_t$ in production function $Y_t = A_tN_t^{1-\alpha}$) seems to be dropped and not compared with model's prediction that can be inferred from coefficients and how reduced-form equations got derived. 

Why is this comparison not often done? This seems to be an important indicator for a model..
It's somewhat public knowledge that Macroeconomic models don't fit labor data without


High Frisch Elasticity (but Rogerson, Prescott try to argue for that)
Preference shocks (but, as Chari, Kehoe, McGrattan (AER 2006?) show, we cannot really understand these properly
Search&amp;Matching with weird parametrization or assumed wage rigidity
Some other spooky thing going on that helps fitting the data


So since we know it doesn't work out, why feel bad about posting the correlations and make everyone feel bad? :)

If it were to work out, surely post it though.