Robustness to parametric assumptions in missing data models

Bryan S. Graham, Keisuke Hirano

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


We consider estimation of population averages when data are missing at random. If some cells contain few observations, there can be substantial gains from imposing parametric restrictions on the cell means, but there is also a danger of misspecification. We develop a simple empirical Bayes estimator, which combines parametric and unadjusted estimates of cell means in a data-driven way. We also consider ways to use knowledge of the form of the propensity score to increase robustness. We develop an empirical Bayes extension of a double robust estimator. In a small simulation study, the empirical Bayes estimators perform well. They are similar to fully nonparametric methods and robust to misspecification when cells are moderate to large in size, and when cells are small they maintain the benefits of parametric methods and can have lower sampling variance.

Original languageEnglish (US)
Pages (from-to)538-543
Number of pages6
JournalAmerican Economic Review
Issue number3
StatePublished - May 2011

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics


Dive into the research topics of 'Robustness to parametric assumptions in missing data models'. Together they form a unique fingerprint.

Cite this