The definition of the observed variables has to match the data you give to Dynare. So you obviously need to define a measurement equation for each (!) variable, not only for y (just as specified in the reference I gave you several times). Matching demeaned log differences in the data to the first difference of loglinearized model variables makes sure that the different steady state values of your model and the empirical data do not matter. The reason is that the steady state values cancel out by taking the difference of log deviations from steady state.
which is your observation equation for y. The one for c follows correspondingly. The reason for demeaning the log-differences is that your model variables are made stationary by detrending. Hence, there should be no steady state growth.
That leaves you with a problem for n, where you usually do not take log differences in the data but the simple log. As in the model hours are normalized to be between 0 and 1, which is not the case in the data, you have to specify a measurement equation like
where n_SS is the steady state value from the model. This is because the log deviations from the model are simply n=log(Nobs/N_SS), but you in the data you have
From this, the above equation directly follows.
But you could try to just take demeaned log-differences for all variables and specify the same measurement equation for n as for c and y. Although this is not the standard in the literature (again see Smets/Wouters (2007)), it should also work.