CLUSTER STANDARD ERRORS STATA SERIES

You can learn more about what the robust estimate entails by looking at the Stata FAQ on the matter and at the additional resources it suggests. The two sandwich estimator subcommands are, vce(robust) which uses a Huber/Whites/sandwich estimator and, vce (cluster. Note: While these procedures can be useful, whether or not you use them and whether or not using them will create more accurate results depends on the nature of your data. Jackknifing is a somewhat similar procedure but where bootstrapping does relatively infinite sampling with replacement, Jackknifing does resampling equal to n and each iteration takes exactly one person out of the sample and recalculates the desired statistic. A brief explanation (with minimal math) of bootstrapping can be found here. This makes it useful for populations whose normality is uncertain.

CLUSTER STANDARD ERRORS STATA SERIES

The two replication-based estimators are vce(bootstrap) and vce(jackknife) To oversimplify, bootstrapping takes a series of random samples from the sample and uses this constant sampling with replacement to calculate standard error. Both refer to the matrices and math which underly the procedure.

The two likelihood estimator subcommands are vce(oim) short for observed information matrix and vce(opg) short for outer product of the gradient vectors. The vce option has three major types of variance estimators: likelihood-based, replication-based and sandwich estimators. The vce() option causes Stata to change the way standard error is calculated. The vce option: changing standard error reports for quirky data

Cluster standard errors stata