Finite-sample performance of the robust variance estimator in the presence of missing data

Abstract

Theoretically, the maximum likelihood estimator has the sandwich-type asymptotic variance-covariance matrix under model misspecification. Its empirical estimator, that is called the robust variance estimator, is consistent. Thus, the estimator is asymptotically valid even under model misspecification. In practice, the robust variance estimator is used for computation of standard errors in longitudinal data analysis. Recently, Golden et al. (2019 Econometrics, 7, 1-27) showed that the maximum likelihood estimator retains a sandwich-type asymptotic variance-covariance matrix in the presence of missing data even when the missing-data mechanism is missing not at random. Although they revealed the asymptotic validity of the robust variance estimator in the simultaneous presence of both model misspecification and missing data, its finite-sample performance did not be investigated. In this article, we evaluated the finite-sample performance via simulation studies and clarify its small-sample problems. In addition, we illustrated the robust variance estimator using longitudinal CD4 count data from a randomized double-blind study.

Keywords:

• Asymptotic variance estimation
• Empirical estimator
• Incomplete data
• Maximum likelihood estimator
• Model misspecification

Acknowledgment

We would like to thank the anonymous reviewer for helpful comments.

Additional information

Funding

This work was supported by JSPS KAKENHI Grant Number JP19K11849.