Validation analysis

Dear all, I have estimated my DSGE model (bayesian estimation), the mcmc graphs, the validation ratio of brooks and gelman and also the posterior distribution graphs, all confirm the validation of model, now I have some questions:

  1. Are the IRF plots vaild if they simply fulfill the expectations from theories? or do I have to check the moments, if they are close to moments for real data or not?
  2. If I have to check for moments, how close should they be to the moments of real data? how is it for y gap bellow in your opinion?
    1st moments 2nd moments correlation coef.
    1st model -0.00002 0.0088 0.2716
    2nd model 0.00003 0.0082 0.4129
    real data -0.00064 0.0274 0.483
  3. for analyzing the irf plots, are the numbers meaningful (in log linearized model) or should we just care for the directions and overall responses?

Thanks in advance

With full information estimation, you consider your model to be the data generating process. If the moments of your model are very different from the actual data, that is indeed a problem. Some differences may occur, but having a variable being four times as volatile in the data as in the model is worrisome.

And yes, numbers matter. Why else would you want to use a quantitative model?