Inverse probability weighted M-estimators for sample selection, attrition, and stratification |
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Authors: | Jeffrey M Wooldridge |
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Institution: | (1) Department of Economics, Michigan State University, Marshall Hall, MI 48824-1038 East Lansing, USA |
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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
-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 Classification:
C13, C21, C23I 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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Keywords: | Attrition Inverse probability weighting M-estimator Nonresponse Sample selection Treatment effect |
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