Inverse probability weighted M-estimators for sample selection, attrition and stratification

Jeffrey Wooldridge

No CWP11/02, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract: I provide an overview of inverse probability weighted (IPW) M-estimators for cross section and two-period panel data applications. Under an ignorability assumption, I show that population parameters are identified, and provide straightforward vN-consistent and asymptotically normal estimation methods. I show that estimating a binary response selection model by conditional maximum likelihood leads to a more efficient estimator than using known probabilities, a result that unifies several disparate results in the literature. But IPW estimation is not a panacea: in some important cases of nonresponse, unweighted estimators will be consistent under weaker ignorability assumptions.

JEL-codes: C13 C21 C23 (search for similar items in EconPapers)
Pages: 42 pp.
Date: 2002-03-01
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (267)

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Related works:
Journal Article: Inverse probability weighted M-estimators for sample selection, attrition, and stratification (2002)
Working Paper: Inverse probability weighted M-estimators for sample selection, attrition and stratification (2002)
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