**[XT]** *vce_options* -- Variance estimators

__Syntax__

*estimation_cmd* ... [**,** *vce_options* ...]

*vce_options* Description
-------------------------------------------------------------------------
**vce(oim)** observed information matrix (OIM)
**vce(opg)** outer product of the gradient (OPG)
vectors
**vce(**__r__**obust)** Huber/White/sandwich estimator
**vce(**__cl__**uster** *clustvar***)** clustered sandwich estimator
**vce(**__boot__**strap** [**,** *bootstrap_options*]**)** bootstrap estimation
**vce(**__jack__**knife** [**,** *jackknife_options*]**)** jackknife estimation

**nmp** use divisor N - P instead of the
default N
__s__**cale(x2**|**dev**|**phi**|*#***)** override the default scale
parameter; available only with
population-averaged models
-------------------------------------------------------------------------

__Description__

This entry describes the *vce_options*, which are common to most xt
estimation commands. Not all the options documented below work with all
xt estimation commands; see the documentation for the particular
estimation command. If an option is listed there, it is applicable.

The **vce()** option 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 **pweight**s 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 to say, the observations are
independent across groups (clusters) but not necessarily within
groups. *clustvar* specifies to which group each observation belongs,
for examples, **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 nonparametric 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**.

**nmp** specifies that the divisor N-P be used instead of the default N,
where N is the total number of observations and P is the number of
coefficients estimated.

**scale(x2**|**dev**|**phi**|*#***)** overrides the default scale parameter. By default,
**scale(1)** is assumed for the discrete distributions (binomial,
negative binomial, and Poisson), and **scale(x2)** is assumed for the
continuous distributions (gamma, Gaussian, and inverse Gaussian).

**scale(x2)** specifies that the scale parameter be set to the Pearson
chi-squared (or generalized chi-squared) statistic divided by the
residual degrees of freedom, which is recommended by McCullagh and
Nelder (1989) as a good general choice for continuous distributions.

**scale(dev)** sets the scale parameter to the deviance divided by the
residual degrees of freedom. This option provides an alternative to
**scale(x2)** for continuous distributions and for over- or
underdispersed discrete distributions.

**scale(phi)** specifies that the scale parameter be estimated from the
data. **xtgee**'s default scaling makes results agree with other
estimators and has been recommended by McCullagh and Nelder (1989) in
the context of GLM. When comparing results with calculations made by
other software, you may find that the other packages do not offer
this feature. In such cases, specifying **scale(phi)** should match
their results.

**scale(***#***)** sets the scale parameter to *#*. For example, using **scale(1)**
in **family(gamma)** models results in exponential-errors regression (if
you assume independent correlation structure).

__Remarks__

When working with panel-data models, we strongly encourage you to use the
**vce(bootstrap)** or **vce(jackknife)** options instead of the corresponding
prefix command. For example, to obtain jackknife standard errors with
**xtlogit**, type

**. webuse clogitid**
**. xtlogit y x1 x2, fe vce(jackknife)**

If you wish to specify more options to the bootstrap or jackknife
estimation, you can include them within the **vce()** option. Below we refit
our model requesting bootstrap standard errors based on 300 replications,
we set the random-number seed so that our results can be reproduced, and
we suppress the display of the replication dots.

**. xtlogit y x1 x2, fe vce(bootstrap, reps(300) seed(123) nodots)**

__Reference__

McCullagh, P., and J. A. Nelder. 1989. *Generalized Linear Models*. 2nd
ed. London: Chapman & Hall/CRC.