A weighted estimating equation for linear regression with missing covariate data

2002 ◽  
Vol 21 (16) ◽  
pp. 2421-2436 ◽  
Author(s):  
Michael Parzen ◽  
Stuart R. Lipsitz ◽  
Joseph G. Ibrahim ◽  
Steven Lipshultz
Keyword(s):  
Linear Regression ◽  
Estimating Equation ◽  
Covariate Data ◽  
Missing Covariate Data ◽  
Weighted Estimating Equation

scholarly journals IDENTIFICATION AND INFERENCE ON REGRESSIONS WITH MISSING COVARIATE DATA

2015 ◽  
Vol 33 (1) ◽  
pp. 196-241 ◽  
Author(s):  
Esteban M. Aucejo ◽  
Federico A. Bugni ◽  
V. Joseph Hotz
Keyword(s):  
Missing Data ◽  
Moment Condition ◽  
Special Focus ◽  
Case Scenario ◽  
Conditional Moment ◽  
Worst Case ◽  
Covariate Data ◽  
Missing Covariate Data ◽  
Missing Data Problem ◽  
Scenario Approach

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 nontrivial identifying power and are relatively easy to use and combine for inferential purposes.

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