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. |
|---|---|
| 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 |
| _version_ | 1866809509531353088 |
|---|---|
| author | Wooldridge, Jeffrey M. |
| author_facet | Wooldridge, Jeffrey M. |
| author_role | author |
| contributor_name_str_mv | Repositório Científico de Acesso Aberto da ULisboa |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Wooldridge, Jeffrey M.\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Repositório Científico de Acesso Aberto da ULisboa |
| datacite.creators.creator.creatorName.fl_str_mv | Wooldridge, Jeffrey M. |
| datacite.date.Accepted.fl_str_mv | 2002-08-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2018-05-23T09:34:20Z |
| datacite.date.embargoed.fl_str_mv | 2018-05-23T09:34:20Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_16ec |
| datacite.subjects.subject.fl_str_mv | Attrition Inverse probability weighting M-estimator Nonresponse Sample selection Treatment effect |
| datacite.titles.title.fl_str_mv | Inverse probability weighted M-estimators for sample selection, attrition, and stratification |
| dc.contributor.none.fl_str_mv | Repositório Científico de Acesso Aberto da ULisboa |
| dc.creator.none.fl_str_mv | Wooldridge, Jeffrey M. |
| dc.date.Accepted.fl_str_mv | 2002-08-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2018-05-23T09:34:20Z |
| dc.date.embargoed.fl_str_mv | 2018-05-23T09:34:20Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10400.5/15460 |
| dc.language.none.fl_str_mv | por |
| dc.publisher.none.fl_str_mv | Springer Verlag |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_16ec |
| dc.subject.none.fl_str_mv | Attrition Inverse probability weighting M-estimator Nonresponse Sample selection Treatment effect |
| dc.title.fl_str_mv | Inverse probability weighted M-estimators for sample selection, attrition, and stratification |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | 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. |
| dirty | 0 |
| eu_rights_str_mv | restrictedAccess |
| format | article |
| fulltext.url.fl_str_mv | https://repositorio.ulisboa.pt/bitstreams/09584eab-40e2-401b-8d86-14deba8226c1/download |
| id | ul_a48a7a41bd48dbddfc866c2be449cd00 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10400.5/15460 |
| instacron_str | ul |
| institution | Universidade de Lisboa |
| instname_str | Universidade de Lisboa |
| language | por |
| network_acronym_str | ul |
| network_name_str | Repositório da Universidade de Lisboa |
| oai_identifier_str | oai:repositorio.ulisboa.pt:10400.5/15460 |
| organization_str_mv | urn:organizationAcronym:ul |
| person_str_mv | Wooldridge, Jeffrey M. |
| publishDate | 2002 |
| publisher.none.fl_str_mv | Springer Verlag |
| reponame_str | Repositório da Universidade de Lisboa |
| repository_id_str | urn:repositoryAcronym:ul |
| service_str_mv | urn:repositoryAcronym:ul |
| spelling | porSpringer Verlagpt_PTI 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.application/pdfpt_PTInverse probability weighted M-estimators for sample selection, attrition, and stratificationWooldridge, Jeffrey M.HostingInstitutionOrganizationalRepositório Científico de Acesso Aberto da ULisboae-mailmailto:repositorio@reitoria.ulisboa.ptrepositorio@reitoria.ulisboa.ptISSNIsPartOf1617-982X (print)ISSNIsPartOf1617-9838 (online)DOIIsPartOf10.1007/s10258-002-0008-x2018-05-23T09:34:20Z2002-082002-08-01T00:00:00ZHandlehttp://hdl.handle.net/10400.5/15460http://purl.org/coar/access_right/c_16ecrestricted accessAttritionInverse probability weightingM-estimatorNonresponseSample selectionTreatment effect185835 bytesliteraturehttp://purl.org/coar/resource_type/c_6501journal articlehttp://purl.org/coar/access_right/c_16ecapplication/pdffulltexthttps://repositorio.ulisboa.pt/bitstreams/09584eab-40e2-401b-8d86-14deba8226c1/downloadPortuguese Economic Journal12117139Lisboa |
| spellingShingle | Inverse probability weighted M-estimators for sample selection, attrition, and stratification Wooldridge, Jeffrey M. Attrition Inverse probability weighting M-estimator Nonresponse Sample selection Treatment effect |
| status | SINGLETON |
| subject.fl_str_mv | Attrition Inverse probability weighting M-estimator Nonresponse Sample selection Treatment effect |
| title | Inverse probability weighted M-estimators for sample selection, attrition, and stratification |
| title_full | Inverse probability weighted M-estimators for sample selection, attrition, and stratification |
| title_fullStr | Inverse probability weighted M-estimators for sample selection, attrition, and stratification |
| title_full_unstemmed | Inverse probability weighted M-estimators for sample selection, attrition, and stratification |
| title_short | Inverse probability weighted M-estimators for sample selection, attrition, and stratification |
| title_sort | Inverse probability weighted M-estimators for sample selection, attrition, and stratification |
| topic | Attrition Inverse probability weighting M-estimator Nonresponse Sample selection Treatment effect |
| topic_facet | Attrition Inverse probability weighting M-estimator Nonresponse Sample selection Treatment effect |
| url | http://hdl.handle.net/10400.5/15460 |
| visible | 1 |