Problem with estimation_params

Hi, thanks for the patience!
The problem of the steady state seems solved (no more red dots). however, the mode check plot and the estimation results are still strange. The likelihood is very small and the Hessian eigenvalues are equal! what does it mean?

This is from the log:

Initial value of the log posterior (or likelihood): -6972520.5243
Gradient norm 17418.6617
Minimum Hessian eigenvalue 7568286935.463
Maximum Hessian eigenvalue 7568286935.463

Iteration 1
Predicted improvement: 0.020044812
lambda = 1; f = 6972520.4849095
lambda = 1.9332; f = 6972520.4481676
lambda = 3.7372; f = 6972520.3771577
lambda = 7.2247; f = 6972520.2399525

                                     Norm of      First-order   Trust-region

Iteration Func-count f(x) step optimality radius
0 1 9.31346e-11 9.28e-06 1
1 2 1.31201e-21 0.000275185 3.63e-11 1

Equation solved.

fsolve completed because the vector of function values is near zero
as measured by the selected value of the function tolerance, and
the problem appears regular as measured by the gradient.

Did you use demeaned growth rates? If yes, please provide the newest version.

no, the previous run is with differenced data. I’ll try with demeaned growth rates and see what happens. the reason why I haven’t done it sofar is that the model is in levels and the means of the theoretical variables are not zero. thanks again!

Dear Pfeifer,
please find attached the log with demeaned growth rates, something is still wrong… thanks


the attachment
codici (688 KB)

Your calibrated standard deviation for the shock makes no sense. Your data is percent times 100. Try using

c_obs = (c-mean(c))/100;
and estimating the standard deviation, for example

estimated_params ; b, normal_pdf, 1.16,0.05; teta, normal_pdf, 5.3181, 0.05; stderr zz, gamma_pdf, 0.1, 5; end;
You need to adjust the prior according to your tastes.

many many thanks! I finally see where the problem came from and now everything works fine.