All estimation commands that allowed options
robust and
cluster() now allow option
vce(vcetype).
vce() specifies how the variance–covariance
matrix of the estimators (and hence standard errors) are to be calculated.
This syntax was introduced in Stata 9, with options such as
vce(bootstrap),
vce(jackknife), and
vce(oim).
In Stata 10, option vce() is extended to
encompass the robust (and optionally clustered) variance calculation.
Where you previously typed
. estimationcommand ..., robust
you are now to type
. estimationcommand ..., vce(robust)
and where you previously typed
. estimationcommand ..., robust cluster(clustervar)
with or without the robust, you are now to type
. estimationcommand ..., vce(cluster clustervar)
You can still type the old syntax, but it is undocumented. The new syntax
emphasizes that the robust and cluster calculation affects standard
errors, not coefficients. See [R]
vce_option.
In accordance with this change, estimation commands now have a term for
their default variance calculation. Thus, you will see things like
vce(ols), and
vce(gnr). Here is what they all mean:

vce(ols). The variance estimator for
ordinary least squares; an
s^{2}(X′X)^{−1}type calculation.

vce(oim). The observed information matrix based on the
likelihood function; a
(−H)^{−1}type
calculation, where H is the Hessian matrix.

vce(conventional). A generic term to
identify the conventional variance estimator associated with the model.
For instance, in the Heckman twostep estimator,
vce(conventional) means the Heckmanderived
variance matrix from an augmented regression. In two different
contexts, vce(conventional) does not
necessarily mean the same calculation.

vce(analytic). The variance estimator
derived from first principles of statistics for means, proportions,
and totals.

vce(gnr). The variance matrix based on an
auxiliary regression, which is analogous to
s^{2}(X′X)^{−1}
generalized to nonlinear regression. gnr
stands for Gauss–Newton regression.

vce(linearized). The variance matrix
calculated by a firstorder Taylor approximation of the statistic,
otherwise known as the Taylor linearized variance estimator, the
sandwich estimator, and the White estimator. This is identical to
vce(robust) in other contexts.
The above are used for defaults. vce() may also be

vce(robust). The variance matrix calculated by the
sandwich estimator of variance, VDVtype calculation,
where V is the conventional variance matrix and
D is the outer product of the gradients,
Σ_{i} g_{i}g′_{i}.

vce(clustervarname).
The clusterbased version of vce(robust)
where sums are performed within the groups formed by
varname, which is equivalent to assuming that
the independence is between groups only, not between observations.

vce(hc2) and
vce(hc3). Calculated similarly as
vce(robust) except that different scores are
used in place of the gradient vectors g_{i}.

vce(opg). The variance matrix calculated by
the outer product of the gradients; a
(Σ_{i} g_{i}g′_{i})^{−1}
calculation.

vce(jackknife). The variance matrix
calculated by the jackknife, including delete one, delete
n, and the clusterbased jackknife.

vce(bootstrap). The variance matrix
calculated by bootstrap resampling.
You do not need to memorize the above; the documentation for the
individual commands, and their corresponding dialog boxes, make
clear what is the default and what is available.