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Identification and inference on regressions with missing covariate data

Esteban M. Aucejo, Federico Bugni and V. Joseph Hotz ()

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: This paper examines the problem of identification and inference on a conditional moment condition model with missing data, with special focus on the case when the conditioning covariates are missing. We impose no assumption on the distribution of the missing data and we confront the missing data problem by using a worst case scenario approach. We characterize the sharp identified set and argue that this set is usually too complex to compute or to use for inference. Given this difficulty, we consider the construction of outer identified sets (i.e. supersets of the identified set) that are easier to compute and can still characterize the parameter of interest. Two different outer identification strategies are proposed. Both of these strategies are shown to have non-trivial identifying power and are relatively easy to use and combine for inferential purposes.

Keywords: missing data; missing covariate data; partial identification; outer identified sets; inference; confidence sets (search for similar items in EconPapers)
JEL-codes: C01 C10 C20 C25 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
Date: 2017-02-01
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Published in Econometric Theory, 1, February, 2017, 33(1), pp. 196-241. ISSN: 0266-4666

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http://eprints.lse.ac.uk/62524/ Open access version. (application/pdf)

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Journal Article: IDENTIFICATION AND INFERENCE ON REGRESSIONS WITH MISSING COVARIATE DATA (2017) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:62524

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