Bayesian IRF Graphs

[scholar_26]

Could you please explain how to interpret bayesian graphs?
And why dont they turn zero after a specific period? (like attached results)

[jpfeifer]

From a soon to be published internal documentation:

Orthogonalized shock to shock_x:

Bayesian IRF plot generated by the bayesian_irf-option of the estimation-command. It is stored in the Output-subfolder. Generally, these IRFs are similar to the ones displayed with stoch_simul and use the same orthogonalization scheme. The main difference is that the stoch_simul-IRFs are computed at the calibrated parameter combination, while the Bayesian IRFs are the mean impulse responses (not to be confused with the IRFs at the mean). The gray shaded areas provide highest posterior density intervals (Highest Posterior Density Interval (HPDI)). If you want to compute classical IRFs after estimation, use stoch_simul after estimation as the latter will set the parameters to the posterior mode/mean, depending on whether you use maximum likelihood or Bayesian estimation. More information can be found in Adjemian, Bastani, Juillard, Karamé, Mihoubi, Perendia, Pfeifer, Ratto, and Villemot (2011)



Note that in your case the IFRs will go back to 0 if you increase the time horizon.

[jpfeifer]

Update: The documentation is now available as Pfeifer (2014): An Introduction to Graphs in Dynare at https://sites.google.com/site/pfeiferecon/dynare

[Daniel Bendel]

Thanks man! This is a really helpful documentation!!