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 \(\sqrt{N}\)-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.
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JEL Classification:
C13, C21, C23
I would like to thank Bo Honoré, Christophe Muller, Frank Windmeijer, and the participants at the CeMMAP/ESCR Econometric Study Group Microeconometrics Workshop for helpful comments on an earlier draft.
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Wooldridge, J.M. Inverse probability weighted M-estimators for sample selection, attrition, and stratification. Portuguese Economic Journal 1, 117–139 (2002). https://doi.org/10.1007/s10258-002-0008-x
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DOI: https://doi.org/10.1007/s10258-002-0008-x