  In this paper we consider an extension of the beta regression model proposed
by Ferrari and Cribari-Neto (2004). We extend their model in two different
ways, first, we let the regression structure be nonlinear, second, we allow a
regression structure for the precision parameter, moreover, this regression
structure may also be nonlinear. Generally, the beta regression is useful to
situations where the response is restricted to the standard unit interval and
the regression structure involves regressors and unknown parameters. We derive
general formulae for second-order biases of the maximum likelihood estimators
and use them to define bias-corrected estimators. Our formulae generalizes the
results obtained by Ospina et al. (2006), and are easily implemented by means
of supplementary weighted linear regressions. We also compare these
bias-corrected estimators with three different estimators which are also
bias-free to the second-order, one analytical and the other two based on
bootstrap methods. These estimators are compared by simulation. We present an
empirical application.
