- Can you estimate a linear regression with the same variable set? Maybe one of your
variables has no variation, or otherwise defective? If -
**reg**- fails, a non-linear estimator is unlikely to succeed. - Insignificant variables are flat spots in the liklihood function. What happens if you remove most of the independent variables and keep only a few very significant ones? Does it converge then? If so, add the other variables back slowly and identify the problematic ones for disposal. This is especially important for high-dimensionality problems.
- Are your data items of wildly different magnitudes? Year and year cubed are less of a
strain on the optimizer if rescaled to (year-1980) and (year-1980)^3 (assuming 1980 is the
mean year). As a bonus the coeficients are easier to understand. Scale the data so that all
variables fall into similar ranges.
There are several -

**maximize_options**- that can be useful. Seehttp://www.stata.com/manuals13/rmaximize.pdf

for the Stata documentation, but here are my sugestions:

- -
**trace**- displays the parameter values at each iteration. Are there pairs of variables that are shooting off to plus or minus infinity? That indicates a perfect predictor. If the liklihood continues to improve, with stablizing parameter values, that is progress. - -
**tolerance**- allows you to loosen the convergence criterion. If the optimizer seems to be stuck at a particular place, perhaps that is the optimum? - -
**from**- allows you to specify starting values. This is especially appropriate for the situation where "It used to converge, but doesn't now". - -
**technique**- allows you to specify which of several hill-climbing techniques should be used, or which combination. Where one fails, another may succeed. Furthermore, what is appropriate at the initial values may not be optimal near convergence. - -
**difficult**- is said to be useful when the optimizer complains "not concave".