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Dear all,

I am trying to estimate a model using Bayesian estimation method. The model with calibrations is working properly. But while estimating I get the following error: An element of the Jacobian is not finite or NaN.
And the explanation below the error: The problem most often occurs, because a variable with exponent smaller than 1 has been initialized to 0. Taking the derivative and evaluating it at the steady state then results in a division by 0.

But I don't see any issue with my variables and equations. Please help!

Your help will be much appreciated.

You can find the .mod file attached.

You currently do not have a proper full calibration in your file before calling

Code: Select all

steady;

Therefore the warning

Warning: Some of the parameters have no value (bettald, bettalag, bettar, bettaz, bettayrus, alfapi, alfay, alfaz, lambda, delta, phi_i,
phi_pi, phi_y, bettaldrus, bettalagrus, bettarrus, alfapirus, alfayrus, phirus_i, phi_pirus, phi_yrus, rho_rrus, rho_r) when using steady.
If these parameters are not initialized in a steadystate file or a steady_state_model-block, Dynare may not be able to solve the model...

Dear Mr. Pfeifer,

Thank you very much for your quick response.

I added in the model parameter values (calibrations) for all the parameters, (still it does not make sense for me, since I followed the Dyanre User Guid and according to it I don't need to provide parameter values (only for the ones I don't want to estimate)), anyway, the model gave me graphs of the distributions. But now I have another error: You did not declare endogenous variables after the estimation/calib_smoother command.

Will be very happy if you can help me out.

By the way, last time I forgot to attach my data file. Maybe the problem comes from there..

Actually I found the reason of the error, it was occurring because in the data file for the date-s column I actually wrote "date", when I deleted it and left empty the code partly worked. I have other errors too, but as I understand there are connected with ill-chosen priors.