Continuous updating gmm estimator
The GMM estimators are known to be consistent, asymptotically normal, and efficient in the class of all estimators that do not use any extra information aside from that contained in the moment conditions.
In econometrics and statistics, the generalized method of moments (GMM) is a generic method for estimating parameters in statistical models.
Demo1: Demonstration of the generalization process using Gaussian Mixture Regression (GMR).
Several studies have examined the performance of GMM in precisely the context that I was using it in my own example. Among his conclusions (p.397): So, where does that leave us?
Three relevant studies are those of Tauchen (1986), Kocherlakota (1990), and Hansen et al. I'm glad that I used the continuous-updating version of the GMM estimator in my illustration.
The main practical hurdle is getting the moment conditions for the estimators in the different steps.
If the steps involve ML, those first-derivative conditions can be directly translated to moment conditions.So, I'm more than sympathetic to the general point that Stephen made.