Stata program fitting a two parameter beta distribution, optionally dependent on covariates. The beta distribution is a flexible model for a dependent variable that is bounded between zero and one, for example a proportion. `betafit` allows two parameterizations, besides the traditional parameterization in terms of two shape parameters it also alows a more interpretable alternative (regression-like) parameterization in terms of a location and scale parameter, which was independently proposed by Philip Paolino (2001), Silvia Ferrari and Francisco Cribari-Neto (2004), and Michael Smithson and Jay Verkuilen (2006).

This package can be installed by typing in Stata:
`
ssc install betafit
`

- Presentation held at the 2010 German Stata Users' meeting on analyzing proportions with
`betafit`,`zoib`, the fractional logit,`dirifit`, and`fmlogit`. - Presentation held at the London Stata Users' meeting 2006 on analyzing proportions with
`betafit`,`dirifit`, and other Stata programs. - Short technical text on how to get from the probability density function shown in the above presentation to the likelihood function used in
`betafit`. - summary on ssc
- helpfiles:
- certification script used for version 1.1.7 (April 08, 2011).
- Announcement on statalist of update including the alternative parameterizations.
- Announcement on Statalist of update including
`dbetafit`which calculates various marginal effects. - Resources for estimating beta regression in SPSS, R, and SAS, by Michael Smithson and Jay Verkuilen.

Ferrari, S.L.P. and Cribari-Neto, F. (2004). Beta regression for modelling rates and
proportions. *Journal of Applied Statistics* 31(7): 799-815.

Paolino, P. (2001). Maximum likelihood estimation of models with beta-distributed dependent
variables. *Political Analysis* 9(4): 325-346. link

Smithson, M. and Verkuilen, J. (2006) A better lemon squeezer? Maximum likelihood regression with beta-distributed dependent variables. *Psychological Methods* 11(1): 54-71.

. use http://fmwww.bc.edu/repec/bocode/c/citybudget.dta, clear(Spending on different categories by Dutch cities in 2005)

. betafit governing , mu(minorityleft noleft )initial: log likelihood =

718.24584rescale: log likelihood =718.24584rescale eq: log likelihood =718.24584Iteration 0: log likelihood =718.24584Iteration 1: log likelihood =732.19835Iteration 2: log likelihood =738.15983Iteration 3: log likelihood =738.1705Iteration 4: log likelihood =738.1705ML fit of beta (mu, phi) Number of obs =

394Wald chi2(2) =40.19Log likelihood =738.1705Prob > chi2 =0.0000------------------------------------------------------------------------------ | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- minorityleft |

-.0407548 .0639882 -0.64 0.524 -.1661693 .0846598noleft |.2520844 .0631474 3.99 0.000 .1283178 .375851_cons |-2.332194 .0539375 -43.24 0.000 -2.43791 -2.226479-------------+---------------------------------------------------------------- /ln_phi |4.013526 .0715673 56.08 0.000 3.873256 4.153795-------------+---------------------------------------------------------------- phi |55.34165 3.960652 48.09876 63.6752------------------------------------------------------------------------------

.