Prof @Pfeiffer, I’m running a small open economy with households, heterogeneous firms, monetary and fiscal policy to study the pass-through transmissions on EME countries.
The Bayesian estimation is ok computing with option 9 and based on quarterly data (DATABfiltro2), I got an acceptable convergence, but when I check the IRFs, for example, the shock ‘wbar’ that measures the UIRP effect (uncovered interest rate parity deviations) on the nominal exchange variations (variable “piis”) i get the appended figure “CodEconModel_Bayesian_IRF_sigma_wbar_1” (check piis_obs), where the shock is -0.2% (revaluated exchange) in the first period and then jump to 0.2% in the 5th quarterly (depreciated exchange), but this look unusual from an economic point of view or from the usual IRF behavior. This kind of jump for 1st period to 5th period happened in other variables too.
Then, i got the IRF from the stochastic simulation and i see a different dynamic (check “CodEconModel_IRF_sigma_wbar”), some more realistic. The original data has been prefilter by HP before passed to the estimation.
I’d like to have a smoother IRF behavior, but i don’t know if this is happening by a seasonal behavior latent in the observable variables or something else or another thing I haven’t seen. I’d appreciate some recommendation to check. I append the code and data, it was run with dynare 6.2 on matlab 2024b.
Thank you so much for any suggestion,
jcsc,
CodEconModel.mod (14.9 KB)
CodEconModel_Bayesian_IRF_sigma_wbar_1.eps (106.0 KB)
CodEconModel_IRF_sigma_wbar.eps (29.8 KB)
DATABfiltro2.xlsx (40.5 KB)