Likelihood only!

[jpfeifer]

You cannot have a mixture. You must either go full ML or full Bayesian. Everything in between will crash.

[Glm]

Oki. But I also tried a full Bayesian where I put priors on all parameters (that's the only difference between "s_and_w_ml.mod" and "s_and_w_prior.mod"). Although it doesn't work the same way that "s_and_w_ml.mod" does, that is, allows me to evaluate the likelihood value only for a given parameter vector.

[jpfeifer]

Then provide the final file that does not work.

[Glm]

No worries, I found the problem myself. Some of the parameters were outside of the prior bounds which in turn generated the error log likelihood -1e+8.

But thanks anyways for all your help!

\\\Glm

[Glm]

If i write like this in the parameter block

constebeta=0.16;
cbeta=1/(1+constebeta/100);

and like this in the model block

x*cbeta=gamma+y*zeta;


will Dynare take into account that cbeta is a function of constebeta when I estimate with regard to constebeta?

\\\Glm

[jpfeifer]

Calibration only once updates the other parameters depending on the estimated one. That’s why you should use model-local variables (the ones with the pound operator) or a steady state file. See Remark 4 (Parameter dependence and the use of model-local variables) in Pfeifer(2013): "A Guide to Specifying Observation Equations for the Estimation of DSGE Models" https://sites.google.com/site/pfeiferecon/Pfeifer_2013_Observation_Equations.pdf.