We are currently experimenting with different sets of observables for a DSGE model in order to find the set with which the model performs best. In this process, the endogenous variables of the model fall into three distinct classes:

(1) latent variables, for which there are no empirical time series,

(2) observables, which are declared in the “varobs” statement and for which we have empirical data,

(3) “potential observables”, which are *not* included in the “varobs” statement, but for which we have data (which is assigned to the variables in the data reading routine).

How does Dynare treat these “potential observables”? Our assumption was that they would be treated the same as the latent variables, i.e. that it should not matter at all if some empirical values are assigned to these variables in the data reading routine.

However, it does seem to matter after all. Results of the estimation diverge dependent on whether or not we assign empirical values to these potential observables. In certain cases it looks like if Dynare fits the shocks to match these potential observables even though they are not in the “varobs” list.

Can anyone make sense of this? Are you only allowed to assign empirical values to the observables that are in the “varobs” list? Shouldn’t Dynare then give a warning whenever this is not the case?