Nonignorable missing data, single index propensity score and profile synthetic distribution function
Publication Type
Journal Article
Version
acceptedVersion
Publication Date
2-2022
Abstract
In missing data problems, missing not at random is difficult to handle since the response probability or propensity score is confounded with the outcome data model in the likelihood. Existing works often assume the propensity score is known up to a finite dimensional parameter. We relax this assumption and consider an unspecified single index model for the propensity score. A pseudo-likelihood based on the complete data is constructed by profiling out a synthetic distribution function that involves the unknown propensity score. The pseudo-likelihood gives asymptotically normal estimates. Simulations show the method compares favorably with existing methods.
Keywords
Missing not at random, Nonignorable missing, Pseudo-conditional likelihood, Single index model, Synthetic distribution
Discipline
Econometrics | Economics
Research Areas
Econometrics
Publication
Journal of Business and Economic Statistics
Volume
40
Issue
2
First Page
715
Last Page
717
ISSN
0735-0015
Identifier
10.1080/07350015.2020.1860065
Publisher
Taylor & Francis
Embargo Period
4-29-2021
Citation
CHEN, Xuerong; LEUNG, Denis H. Y.; and QIN, Jing.
Nonignorable missing data, single index propensity score and profile synthetic distribution function. (2022). Journal of Business and Economic Statistics. 40, (2), 715-717.
Available at: https://ink.library.smu.edu.sg/soe_research/2466
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1080/07350015.2020.1860065