Can double dipping be reasonable?

I found a paper where the authors used bayesian methods to estimate asymmetric effects in impulse response functions. In short the estimation procedure is:

1. Calculate a VAR and Impulse responses (no matter what identification strategy).
2. Express this IRF´s as a a set of gaussian basis function. (This reduces the number of parameter)
3. Use this estimates as the initial guess (=prior?) of a Metropolis-Hastings Algorithm.

All steps use the same data.

I'm a bit confused if it makes sense to extract the prior information from the same data where the MCMC algorithm will be used in the next step? I learned that "double dipping" is a problem in bayesian statistics. Since it is a relatively well-known paper, I assume that there is an explanation for this point, but I don't get it.

In my opinion it's often safe to extract some extremely general information from a dataset using empirical Bayes. E.g. extract the range of possible values |max - min| of the data in order to calibrate, say, a prior on the variance of some parameter - is it 10^1 or 10^6? But not, e.g. what was the value of unit $$i$$ at time $$t$$?