Publicação
Inverse probability weighted M-estimators for sample selection, attrition, and stratification
| Resumo: | I provide an overviewof 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 √ 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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| Autores principais: | Wooldridge, Jeffrey M. |
| Assunto: | Attrition Inverse probability weighting M-estimator Nonresponse Sample selection Treatment effect |
| Ano: | 2002 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Universidade de Lisboa |
| Idioma: | português |
| Origem: | Repositório da Universidade de Lisboa |
| Resumo: | I provide an overviewof 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 √ 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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