Let me clarify my point. Your model variables are in logs and you have done the arbitrary normalization that the steady state of the log nominal exchange rate is 1. Now you have to match this model to the data where the nominal exchange rate is not in logs and its log has not mean 1. The best way is to specify an observation equation that gets rid of the different means (as described in Listing 7 of Pfeifer "A Guide to Specifying Observation Equations for the Estimation of DSGE Models")
That is, you demean the log empirical exchange rate in the Excel file by just subtracting the mean. Then you use an observation equation of the form
- Code: Select all
s-steady_state(s) = s_obs;
telling the model that the observed log exchange rate is one where the mean has been subtracted.
This formulation also should make forecasting easier, because your forecasts are then for the percentage deviation of the exchange rate from its sample mean.
I add a running version.