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Pull Your Small Area Estimates Up by the Bootstraps

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dc.contributor.authorMolina, Isabel
dc.contributor.authorCorral, Paul
dc.contributor.authorNguyen, Minh
dc.date.accessioned2020-05-28T15:42:40Z
dc.date.available2020-05-28T15:42:40Z
dc.date.issued2020-05
dc.description.abstractAfter almost two decades of poverty maps produced by the World Bank and multiple advances in the literature, this paper presents a methodological update to the World Bank's toolkit for small area estimation. The paper reviews the computational procedures of the current methods used by the World Bank: the traditional approach by Elbers, Lanjouw and Lanjouw (2003) and the Empirical Best/Bayes (EB) addition introduced by Van der Weide (2014). The addition extends the EB procedure of Molina and Rao (2010) by considering heteroscedasticity and includes survey weights, but uses a different bootstrap approach, here referred to as clustered bootstrap. Simulation experiments comparing these methods to the original EB approach of Molina and Rao (2010) provide empirical evidence of the shortcomings of the clustered bootstrap approach, which yields biased point estimates. The main contributions of this paper are then two: 1) to adapt the original Monte Carlo simulation procedure of Molina and Rao (2010) for the approximation of the extended EB estimators that include heteroscedasticity and survey weights as in Van der Weide (2014); and 2) to adapt the parametric bootstrap approach for mean squared error (MSE) estimation considered by Molina and Rao (2010), and proposed originally by González-Manteiga et al. (2008), to these extended EB estimators. Simulation experiments illustrate that the revised Monte Carlo simulation method yields estimators that are considerably less biased and more efficient in terms of MSE than those obtained from the clustered bootstrap approach, and that the parametric bootstrap MSE estimators are in line with the true MSEs under realistic scenarios.en
dc.identifierhttp://documents.worldbank.org/curated/en/714341590090749405/Pull-Your-Small-Area-Estimates-up-by-the-Bootstraps
dc.identifier.doi10.1596/1813-9450-9256
dc.identifier.urihttps://hdl.handle.net/10986/33819
dc.languageEnglish
dc.publisherWorld Bank, Washington, DC
dc.relation.ispartofseriesPolicy Research Working Paper;No. 9256
dc.rightsCC BY 3.0 IGO
dc.rights.holderWorld Bank
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/igo
dc.subjectPOVERTY MAPPING
dc.subjectSMALL AREA ESTIMATE
dc.subjectELL
dc.subjectEMPIRICAL BEST
dc.subjectPARAMETRIC BOOTSTRAP
dc.subjectELBERS, LANJOUW AND LANJOUW
dc.subjectHETEROSCEDASTICITY
dc.subjectSURVEY WEIGHTS
dc.subjectMEAN SQUARED ERROR ESTIMATION
dc.subjectMONTE CARLO SIMULATION
dc.titlePull Your Small Area Estimates Up by the Bootstrapsen
dc.typeWorking Paperen
dc.typeDocument de travailfr
dc.typeDocumento de trabajoes
dspace.entity.typePublication
okr.associatedcontenthttps://openknowledge.worldbank.org/handle/10986/36821 Journal article
okr.crossref.titlePull Your Small Area Estimates Up by the Bootstraps
okr.date.disclosure2020-05-21
okr.doctypePublications & Research
okr.doctypePublications & Research::Policy Research Working Paper
okr.docurlhttp://documents.worldbank.org/curated/en/714341590090749405/Pull-Your-Small-Area-Estimates-up-by-the-Bootstraps
okr.guid714341590090749405
okr.identifier.doi10.1596/1813-9450-9256
okr.identifier.externaldocumentum090224b08796a2d4_1_0
okr.identifier.internaldocumentum32057711
okr.identifier.reportWPS9256
okr.importedtrueen
okr.language.supporteden
okr.pdfurlhttp://documents.worldbank.org/curated/en/714341590090749405/pdf/Pull-Your-Small-Area-Estimates-up-by-the-Bootstraps.pdfen
okr.statistics.combined1543
okr.statistics.dr714341590090749405
okr.statistics.drstats754
okr.topicPoverty Reduction::Inequality
okr.topicPoverty Reduction::Small Area Estimation Poverty Mapping
okr.unitPoverty and Equity Global Practice
relation.isAuthorOfPublication562ed170-5271-5853-9c3c-a3ad9e16e176
relation.isAuthorOfPublication.latestForDiscovery562ed170-5271-5853-9c3c-a3ad9e16e176
relation.isSeriesOfPublication26e071dc-b0bf-409c-b982-df2970295c87
relation.isSeriesOfPublication.latestForDiscovery26e071dc-b0bf-409c-b982-df2970295c87
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