## Stata 15 help for vcetype

```
[R] vce_option -- Variance estimators

Syntax

estimation_cmd ... [, vce(vcetype) ...]

vcetype                           Description
-------------------------------------------------------------------------
Likelihood based
oim                               observed information matrix (OIM)
opg                               outer product of the gradient (OPG)
vectors

Sandwich estimators
robust                            Huber/White/sandwich estimator
cluster clustvar                  clustered sandwich estimator

Replication based
bootstrap [, bootstrap_options]   bootstrap estimation
jackknife [, jackknife_options]   jackknife estimation
-------------------------------------------------------------------------

Description

This entry describes the vce() option, which is common to most estimation
commands.  vce() specifies how to estimate the variance-covariance matrix
(VCE) corresponding to the parameter estimates.  The standard errors
reported in the table of parameter estimates are the square root of the
variances (diagonal elements) of the VCE.

Options

+-----------+
----+ SE/Robust +--------------------------------------------------------

vce(oim) is usually the default for models fit using maximum likelihood.
vce(oim) uses the observed information matrix (OIM); see [R] ml.

vce(opg) uses the sum of the outer product of the gradient (OPG) vectors;
see [R] ml.  This is the default VCE when the technique(bhhh) option
is specified; see [R] maximize.

vce(robust) uses the robust or sandwich estimator of variance.  This
estimator is robust to some types of misspecification so long as the
observations are independent; see [U] 20.22 Obtaining robust variance
estimates.

If the command allows pweights and you specify them, vce(robust) is
implied; see [U] 20.24.3 Sampling weights.

vce(cluster clustvar) specifies that the standard errors allow for
intragroup correlation, relaxing the usual requirement that the
observations be independent.  That is, the observations are
independent across groups (clusters) but not necessarily within
groups.  clustvar specifies to which group each observation belongs,
for example, vce(cluster personid) in data with repeated observations
on individuals.  vce(cluster clustvar) affects the standard errors
and variance-covariance matrix of the estimators but not the
estimated coefficients; see [U] 20.22 Obtaining robust variance
estimates.

vce(bootstrap [, bootstrap_options]) uses a bootstrap; see [R] bootstrap.
After estimation with vce(bootstrap), see [R] bootstrap
postestimation to obtain percentile-based or bias-corrected
confidence intervals.

vce(jackknife [, jackknife_options]) uses the delete-one jackknife; see
[R] jackknife.

Remarks

Remarks are presented under the following headings:

Prefix commands
Passing options in vce()

Prefix commands

Specifying vce(bootstrap) or vce(jackknife) is often equivalent to using
the respective prefix command.  Here is an example using jackknife with
regress.

. sysuse auto
. regress mpg turn trunk, vce(jackknife)
. jackknife: regress mpg turn trunk

Here it does not matter whether we specify option vce(jackknife) or

However, vce(jackknife) should be used in place of the jackknife prefix
whenever available because they are not always equivalent.  For example,
to use the jackknife prefix with clogit properly, you must tell jackknife
to omit whole groups rather than individual observations.  Specifying
vce(jackknife) does this automatically.

. webuse clogitid
. jackknife, cluster(id): clogit y x1 x2, group(id)

This extra information is automatically communicated to jackknife by
clogit when the vce() option is specified.

. clogit y x1 x2, group(id) vce(jackknife)

Passing options in vce()

If you wish to specify more options to the bootstrap or jackknife
estimation, you can include them within the vce() option.  Below we
request 300 bootstrap replications and save the replications in
bsreg.dta.

. sysuse auto
. regress mpg turn trunk, vce(bootstrap, rep(300) saving(bsreg))
. bstat using bsreg

```