I can get Dynare to estimate my model based on the log of my data in two ways. But is there a third way?

Use the loglinear argument in the Dynare command estimate [and enter the data without taking logs: Dynare does this for you].

Rewrite the model equations as function[exp(log of data)]. For example if my model equation is
y = (k(1)/dA)^chi * lab^(1  chi), where y=output/technology, k=capital stock/A, dA=A/A(1), lab=labor supply
then I could rewrite this as
exp[lny]=(exp(lnk(1)/exp(dlnA)^chi * lab^(1chi), where lny=log(y), lnk=log(k), and dlnA=log(dA). [Now labor supply does not enter as a loglinear. The data that is to be loglinearized is read in in logs.] 
Will this work? I leave all the model equations unchanged, read in the data I want the log taken of already in logs [so I read in lnY_obs=log(Y_obs)] and use observer equations like this one
lnY_obs  lnY_obs(1) = log(dA) + log(y)  log(y(1));
instead of the usual observer equation [where I read in the data Y_obs, not in logs]
Y_obs/Y_obs(1) = dA * y/y(1);
Will this last method work? It means that none of my model equations will be loglinearized, right? Except the observer equations, which need to be loglinearized so that it will not matter where the Taylor expansion is done.
One other point about method 3: the variables in the model equations are all divided by the trend, A. This ensures that all the variables (except for the observed variables like Y_obs) are stationary. It also probably should ensure that there is no problem with heteroscedasticity. As a result, it seems to us that there is no real need to use a loglinear approximation (and use the log of the data for the estimation). Or are we missing something?
Thank you for any help on this matter.