beta-regression

This is an initial go at adding beta-regression starting from `GenericLikelihoodModel` for feedback.

Here is the mailing list discussion: https://groups.google.com/forum/#!topic/pystatsmodels/3qEp-0YpEIc
and gist: https://gist.github.com/brentp/089c7d6d69d78d26437f

Josef suggested adding analytical derivatives and moving out of `miscmodels`. I will require some hand-holding to implement that so I have left it in `miscmodels` for now.

Currently, it has a separate link for the precision parameter (a + b == phi), but the results are reported in the context of the link for the X covariates. I'm not sure how to separate them out in the summary()

The Z matrix for modeling the precision currently must be specified as a matrix. Perhaps it makes sense to allow a z_formula to the `from_formula` method.

I put the csv data for the tests in the `miscmodels/tests` directory. 

The paper for R's `betareg` includes methods for bias-correction and bias-reduction, which I would like to implement eventually. 

It's not that easy.
missing link_precision:
`start_precision[0] = self.link_precision(start_precision_const)`
and then we are not guaranteed to have a constant in the user defined exog_precision.

more elaborate version: I think we can have a few steps of iterated weighted least squares for both mean and precision models, if the regular optimization fails.