Stata 15 help for vcetype

[R] vce_option -- Variance estimators


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 -------------------------------------------------------------------------


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.


+-----------+ ----+ 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 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 instead use the jackknife prefix.

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

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