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Cited by 199

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Semiparametric efficiency in GMM models with auxiliary data

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Chen, Xiaohong, Hong, Han, Tarozzi, Alessandro·2008·The Annals of Statistics
DOI 10.1214/009053607000000947
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Abstract

We study semiparametric efficiency bounds and efficient estimation of parameters defined through general moment restrictions with missing data. Identification relies on auxiliary data containing information about the distribution of the missing variables conditional on proxy variables that are observed in both the primary and the auxiliary database, when such distribution is common to the two data sets. The auxiliary sample can be independent of the primary sample, or can be a subset of it. For both cases, we derive bounds when the probability of missing data given the proxy variables is unknown, or known, or belongs to a correctly specified parametric family. We find that the conditional probability is not ancillary when the two samples are independent. For all cases, we discuss efficient semiparametric estimators. An estimator based on a conditional expectation projection is shown to require milder regularity conditions than one based on inverse probability weighting.

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Research works printed but not linked: 30

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Metadata

Authors
Chen, X., Hong, H., & Tarozzi, A.
Venue
The Annals of Statistics
Publisher
Institute of Mathematical Statistics
Published
2008-04-01
Volume
36
Issue
2
Pages
808-843
ISSN
0090-5364 (ISSN-L)
DOI
10.1214/009053607000000947
OpenAlex
W2160111894
Cited by
199
References
3
FWCI
11.18
Type
journal article
Language
en
Open access
bronze

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Chen, X., Hong, H., & Tarozzi, A. (2008). Semiparametric efficiency in GMM models with auxiliary data. The Annals of Statistics. https://doi.org/10.1214/009053607000000947

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