Errors-in-variables (EIV) regression is a standard method for consistent estimation in linear models with error-prone covariates. The Stata commands eivreg and sem both can be used to compute the same EIV estimator of the regression coefficients. However, the commands do not use the same methods to estimate the standard errors of the estimated regression coefficients. In this article, we use analysis and simulation to demonstrate that standard errors reported by eivreg are negatively biased under assumptions typically made in latent-variable modeling, leading to confidence interval coverage that is below the nominal level. Thus, sem alone or eivreg augmented with bootstrapped standard errors should be preferred to eivreg alone in most practical applications of EIV regression.

变量误差（EIV）回归是具有易错协变量的线性模型中一致估计的标准方法。Stata命令eivreg和sem均可用于计算回归系数的相同EIV估计量。但是，这些命令没有使用相同的方法来估计估计的回归系数的标准误差。在本文中，我们使用分析和模拟来证明eivreg报告的标准误差在潜变量模型中通常做出的假设下具有负偏差，从而导致置信区间覆盖率低于标称水平。因此，单独进行sem或eivreg在EIV回归的大多数实际应用中，应优先使用自举标准误差进行增强，而不是单独使用eivreg。