I run a mod file successfully with
mode_compute = 6 under Bayesian estimation. However, I get the error:
Error using chol when estimating the parameters under SMM estimation (
mode_compute = 6).
Under SMM estimation, I changed the optimizer to
mode_compute = 4…and I get the message
solver stopped prematurely, so the moments do not match.
I tried using simulated data, but the same problems under SMM estimation (
Error using chol).
Is it a common problem when order=1 in
method_of_moments() and the model is linear?
Finding the optimum is generally hard.
mode_compute=6 is a very inefficient optimizer and within the context of SMM/GMM it does not have the advantage of always providing a positive definite Hessian.
Also, I thought specifying prior distributions in Bayesian estimation maybe sort of help the optimizer. But specifying prior distributions (prior shapes, for example) is not available in SMM/GMM estimation yet.
It’s a quadratic objective function, so any prior will be considered approximately normal. But generally, priors are supported.