# #StackBounty: #time-series #bayesian #econometrics #mcmc Can double dipping be reasonable?

### Bounty: 50

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.

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