Dynare forums

Oh I see, thank you so much!

Should there also be the shock on the liabilities side of the Bank balance sheet? that is

exp(b_h) + exp(b_e) + eps_K_b = exp(d_b) + exp(K_b) ;

I have found all the other impulses to match the Gerali model, however for some reason am finding a positive output shock

Ths shock on the balance sheet is used by the authors when they estimated the model .
Its presence seems to improve a lot the estimation (try by yourself).
But when they simulate the negative capital banking shock they simulate the model using the calibrated parameters at the posterior mean and then they include
a capital shock like my previous post

Hope it helps

Hi everybody.
I am trying to modify the Gerali's paper by introducing an SPV involved in a securitization process. I used the same approach of the paper.
I slightly modified the program "GNSS_P3_version2.mod" posted by somekindofgrinder, but after running Dynare I have this error:


SOLVE: maxit has been reached

SOLVE: maxit has been reached

SOLVE: maxit has been reached
Error in computing likelihood for initial parameter values
??? Error using ==> print_info at 57
Impossible to find the steady state. Either the model doesn't have a
steady state, there are an infinity of steady states, or the guess values
are too far from the solution

Error in ==> initial_estimation_checks at 69
print_info(info, DynareOptions.noprint)

Error in ==> dynare_estimation_1 at 169
oo_ =
initial_estimation_checks(objective_function,xparam1,dataset_,M_,estim_params_,options_,bayestopt_,oo_);

Error in ==> dynare_estimation at 70
dynare_estimation_1(var_list,dname);

Error in ==> newbankingSPV at 819
dynare_estimation(var_list_);

Error in ==> dynare at 120
evalin('base',fname) ;


The question is:
do you think that there is a bug in my program, as well as in my theoretical model, or the only problem stays in wrong initial values?
I am a beginner of Dynare, so I really appreaciate all kind of hints.

Thank you very much in advance for an eventual reply.

I attach the file .mod, the m. file with data and a short note to explain what I have done.

I don't have time to go deep in the equations of your model but just look at the code I hve a couple of remarks

First of all, before embarking in the estimation, calibrate your model in order to check if you obtained coherent results.
If you remove % from the 787 line you can see by yourself the problem.

Another remarks. Where are the observables equations?
you have to define not only the observable variables but also the observable equation to estimate your model.

Thanks a lot Federico! I'll try to fix my mistakes.

Hi all,

I am attempting to augment a policy rule incorporating lending spreads (a la curdia woodford) where the policy rate falls with an increase in the lending spread, into the Gerali et al (2010) model however the Impulse Response Functions to a financial shock seem to be almost reversed: that is - incorporating a negative policy reaction to spreads seems to increase the policy rate when spreads widen.

I was wondering if anyone who was familiar with the Gerali model had any insights into the errors I was possibly making? I have a feeling it relates to the rational expectations of the banks in the model

Appreciate the time you have taken!

I deleted Ale_e_Andre 's SPV part of the code and used it to estimate parameters, but the estimation is still not OK, even I modified the mode_compute=4, 5,6,7,8 9. the data file is your original vesion.
do you know what's wrong?

Hi hrdg,
Looking at the estimation command: why did you put 1 for mh_replic? I put 100 as trial value, and I got results, the program run and we have IRFs. Anyway, you should probably increase the number of replications as in the original paper.
This is my first impression. Check your results in terms of both econometric and economic robustness.
Hope it is helpful.

Hello! I hope you had nice christmas holydays and are motivated to help me on this
I am also replicating the Gerali et al (2010) model. I have the code for the baseline model from the MMB2 model database. Now I am wondering if the authors change the code in some way before simulating the monetary policy shock. For the technology shock everything is fine but when I use the code to simulate a monetary policy shock, especially inflation and the policy rate (but also consumption, output and deposits) react too strongly. I attach my code. Thank you!