Identification and inference on regressions with missing covariate data

Aucejo, Esteban M., Bugni, Federico A. and Hotz, V. Joseph (2017) Identification and inference on regressions with missing covariate data. Econometric Theory, 33 (1). pp. 196-241. ISSN 0266-4666

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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.

Item Type:	Article
Official URL:	http://journals.cambridge.org/action/displayJourna...
Additional Information:	© 2015 Cambridge University Press
Divisions:	Economics
Subjects:	H Social Sciences > HB Economic Theory
JEL classification:	C - Mathematical and Quantitative Methods > C0 - General > C01 - Econometrics C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C10 - General C - Mathematical and Quantitative Methods > C2 - Econometric Methods: Single Equation Models; Single Variables > C20 - General C - Mathematical and Quantitative Methods > C2 - Econometric Methods: Single Equation Models; Single Variables > C25 - Discrete Regression and Qualitative Choice Models
Date Deposited:	29 Jun 2015 08:59
Last Modified:	12 Dec 2024 01:20
Projects:	SES-1123771
Funders:	National Science Foundation
URI:	http://eprints.lse.ac.uk/id/eprint/62524

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