Semiparametric inference with missing data: Robustness to outliers and model misspecification

Eva Cantoni and Xavier de Luna

Econometrics and Statistics, 2020, vol. 16, issue C, 108-120

Abstract: Classical semiparametric inference with missing outcome data is not robust to contamination of the observed data and a single observation can have arbitrarily large influence on estimation of a parameter of interest. This sensitivity is exacerbated when inverse probability weighting methods are used, which may overweight contaminated observations. Inverse probability weighted, double robust and outcome regression estimators of location and scale parameters are introduced, which are robust to contamination in the sense that their influence function is bounded. Asymptotic properties are deduced and finite sample behaviour studied. Simulated experiments show that contamination can be more serious a threat to the quality of inference than model misspecification. An interesting aspect of the results is that the auxiliary outcome model used to adjust for ignorable missingness by some of the estimators, is also useful to protect against contamination. Both adjustment to ignorable missingness and protection against contamination are achieved through weighting schemes. A case study illustrates how the resulting weights can be studied to gain insights on how the two different weighting schemes interact.

Keywords: average causal effects; doubly robust estimator; influence function; inverse probability weighting; outcome regression (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S2452306220300198
Full text for ScienceDirect subscribers only. Contains open access articles

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:16:y:2020:i:c:p:108-120

DOI: 10.1016/j.ecosta.2020.01.003

Access Statistics for this article

Econometrics and Statistics is currently edited by E.J. Kontoghiorghes, H. Van Dijk and A.M. Colubi

More articles in Econometrics and Statistics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().