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. 

something like the following
a bit shortened compared to Ferrari and Cribari-Neto page 8, (use resid_y**2 for sigma_i in denominator)

```
fitted = self.link.inverse(start_fit.fittedvalues)
start_precision_const = (fitted * ( 1 - fitted) / (self.endog - fitted)**2).mean() - 1
start_precision = np.zeros(nz)
start_precision[0] = start_precision_const
```

(I have another branch checked out and cannot switch right now to try it out.)