I found a paper where the authors used bayesian methods to estimate asymmetric effects in impulse response functions. In short the estimation procedure is:
- Calculate a VAR and Impulse responses (no matter what identification strategy).
- Express this IRF´s as a a set of gaussian basis function. (This reduces the number of parameter)
- 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.