Sorry, @superj , yes, I viewed the linked topic before. I remember that csminwel is also a local method. For my situation, set_param_value(...) seems to be adequate. So I’m afraid that I fail to give you further suggestion.
Instead, CMAES is a global method. I attempted to apply CMAES in a Ramsey situation. See
Dynare finds the result more quickly than a two-instrument version of RamseySOE, so we didn’t notice the uniqueness. However, in the two-instrument version, we indeed realized it, very sensitive to our initval guess. So here is my question:
Will CMAES be helpful in Ramsey problem?
(I plan to substitute CMAES for fminsearch function)
Besides, your reply gives me the impression that the upper bound of the welfare of OSR-SOE, by deliberately choosing Taylor parameters, can dominate the lower…
But
CMAES will not help. It’s not about your optimizer in the steady state file converging to something only locally optimal. It’s about Dynare’s routines internally converging to a local optimum. As of right now, there is no way to change this other than trying different initial conditions. But even with a global optimizer, that would be someone should do to make sure results are not driven by initial conditions.
csminwel is a Newton-type local optimizer. If you are close to the global mode, it will quickly converge to this this mode. But it can also converge to a local mode, i.e. fail to find the global one because it is local.
cmaes is a global optimizer. It will be slower but has a higher likelihood of finding the global mode instead of just a local one.
Welcome your ‘‘user experience’’
