Hi,
I’m trying to replicate Akinci & Queralto (2019). The small model (for which log deviations from SS can be solved by hand) is OK, but when trying to replicate their medium scale model I keep on getting into trouble in the computation of the steady state. The static equations have some non-negligible residuals but not sure that is the problem. Any diagnostic tools I could use to detect problem?
Any help would be greatly appreciated.
Thanks!
Akinci_Queralto_medium_forum.mod (29.4 KB)
Hi silvio_gesell,
You could you use model_diagnostics;
It appears that:
MODEL_DIAGNOSTICS: The following endogenous variables aren’t present at the current period in the model:
lambda
lambda_star
R_K_star
Thanks a lot for checking this out Camilo.
I think that’s just convention (can time SDF as t or t+1), and should not matter in steady state anyways. Just in case I have tried switching the timing of those variables to “t” but still getting the same error in computation of steady state.
What I was hoping for is to try to get more info from Dynare of what’s failing in steady state as the error message is quite general; do you know of any way of getting at this?
Thanks again!
The error your model gives, using steady(solve_algo=4,maxit=1000); , is the standard one:
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
Your equations have residuals and I do not have an apparent reason to believe there is “another problem” involved other than the guess of your steady state being incorrect. I would take action in three directions, all equally important for your model
:
- Take care of timing.
- Start by checking equations with larger residuals, see if you can solve the issue with that equation, and sequentially move to other problematic equations with smaller residuals.
- Pay a lot of attention to exp() transformations.
Thanks again Camilo.
Agreed regarding issue with initial guess, just did not know how sensible dynare was to values that were a bit off.
I was proceeding as per your suggestion n2 (and have since corrected timing), but not sure what you mean by “pay a lot of attention to exp() transformations”?
Thanks again
It was more of a general suggestion
, that can be broken down into two parts:
- It is always better to start work on the model without exp() substitution, find the correct steady state that makes the model work, and only then proceed to the exp() substitution.
- Your model is large and equations are long and complex, so when you debug you should check that you have been consistent throughout in your exp() substitution.
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Yes, definitely. Thanks again for your time Camilo!