Inverse probability weighted estimation for general missing data problems

Jeffrey Wooldridge

No 05/04, CeMMAP working papers from Institute for Fiscal Studies

Abstract: I study inverse probability weighted M-estimation under a general missing data scheme. The cases covered that do not previously appear in the literature include M-estimation with missing data due to a censored survival time, propensity scoreestimation of the average treatment effect for linear exponential family quasi-log-likelihood functions, and variable probability sampling with observed retainment frequencies. I extend an important result known to hold in special cases: estimating the selection probabilities is generally more efficient than if the known selection probabilities could be used in estimation. For the treatment effect case, the setup allows for a simple characterization of a double robustness result due to Scharfstein, Rotnitzky, and Robins (1999): given appropriate choices for the conditional mean function andquasi-log-likelihood function, only one of the conditional mean or selection probability needs to be correctly specified in order to consistently estimate the average treatmenteffect.

Date: 2004-04-01
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.cemmap.ac.uk/wp-content/uploads/2020/08/CWP0504.pdf (application/pdf)

Related works:
Journal Article: Inverse probability weighted estimation for general missing data problems (2007)
Working Paper: Inverse probability weighted estimation for general missing data problems (2004)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:azt:cemmap:05/04

DOI: 10.1920/wp.cem.2004.0504

Access Statistics for this paper

More papers in CeMMAP working papers from Institute for Fiscal Studies Contact information at EDIRC.
Bibliographic data for series maintained by Dermot Watson ().