Re: [Gretl-devel] Re: Functions for stochastic frontier analysis

I will leave the DEA analysis to someone else. Personally, I have never dealt with a dataset for which DEA provided a plausible set of results, let alone improved on what ones gets from SFA. Normally, the set of comparators turns out to be empty or too small to be useful.

I will modify the packages to include the description strings and then re-load them to the server. Finally, I have been thinking about the panel data SFA. I will start with the time invariant version which is little more than a set of restrictions on the conventional model - e.g. u[i,t] = u[i] for all t. That should mean that it is possible to produce equivalent versions for all of my cross section models once I understand how panel data can be manipulated conveniently. There is one approach that would facilitate the implementation of estimator. The log-likelihood function has the form sum over cross section units { sum over time periods [ ... ] } Thus, it would be easiest to write the log-likelihood and its derivatives in this structure. However, if I understand the logic of mle correctly, then every observation is treated identically so that what mle hands to the maximiser is the contribution of that observation to the log-likelihood and its gradients. If correct, the problem is manageable but less efficient than if one could take advantage of the structure of the log-likelihood to avoid repetition of various calculations.