Publication:
Is Random Forest a Superior Methodology for Predicting Poverty?: An Empirical Assessment

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dc.contributor.authorSohnesen, Thomas Pave
dc.contributor.authorStender, Niels
dc.date.accessioned2016-04-26T16:49:08Z
dc.date.available2016-04-26T16:49:08Z
dc.date.issued2016-03
dc.description.abstractRandom forest is in many fields of research a common method for data driven predictions. Within economics and prediction of poverty, random forest is rarely used. Comparing out-of-sample predictions in surveys for same year in six countries shows that random forest is often more accurate than current common practice (multiple imputations with variables selected by stepwise and Lasso), suggesting that this method could contribute to better poverty predictions. However, none of the methods consistently provides accurate predictions of poverty over time, highlighting that technical model fitting by any method within a single year is not always, by itself, sufficient for accurate predictions of poverty over time.en
dc.identifierhttp://documents.worldbank.org/curated/en/2016/03/26089791/random-forest-superior-methodology-predicting-poverty-empirical-assessment
dc.identifier.doi10.1596/1813-9450-7612
dc.identifier.urihttps://hdl.handle.net/10986/24154
dc.languageEnglish
dc.language.isoen_US
dc.publisherWorld Bank, Washington, DC
dc.relation.ispartofseriesPolicy Research Working Paper;No. 7612
dc.rightsCC BY 3.0 IGO
dc.rights.holderWorld Bank
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/igo/
dc.subjectPREDICTIONS
dc.subjectPOOR HOUSEHOLD
dc.subjectCONSUMPTION EXPENDITURES
dc.subjectHOUSEHOLD SIZE
dc.subjectHOUSEHOLD SURVEY
dc.subjectAGRICULTURAL GROWTH
dc.subjectCONSUMPTION
dc.subjectPOVERTY REDUCTION
dc.subjectIMPACT ON POVERTY
dc.subjectPOVERTY RATES
dc.subjectERRORS
dc.subjectFARMER
dc.subjectPOVERTY RATE
dc.subjectFOOD CONSUMPTION
dc.subjectINCOME
dc.subjectLINEAR REGRESSION
dc.subjectPOVERTY RATES
dc.subjectPOVERTY ESTIMATES
dc.subjectALGORITHMS
dc.subjectHOUSEHOLD SURVEYS
dc.subjectPROGRAMS
dc.subjectCONSUMPTION DATA
dc.subjectHOUSEHOLD SIZE
dc.subjectHOUSING
dc.subjectPOVERTY ESTIMATES
dc.subjectAGRICULTURAL PRACTICES
dc.subjectIMPACTS
dc.subjectNATIONAL POVERTY
dc.subjectSAMPLES
dc.subjectRURAL
dc.subjectVARIABLES
dc.subjectMEASUREMENT
dc.subjectCOUNTING
dc.subjectHOUSEHOLD BUDGET
dc.subjectCONSUMPTION AGGREGATE
dc.subjectQUALITY
dc.subjectSURVEYS
dc.subjectSOCIAL ASSISTANCE
dc.subjectMEASURES
dc.subjectINSTRUMENTS
dc.subjectPOVERTY REDUCTION
dc.subjectTARGETING
dc.subjectRANDOM SAMPLES
dc.subjectAGRICULTURAL PRACTICES
dc.subjectCONSUMPTION EXPENDITURE
dc.subjectRURAL AREAS
dc.subjectCROSS‐SECTION DATA
dc.subjectWELFARE MEASURES
dc.subjectCROSS‐SECTION DATA
dc.subjectWELFARE INDICATORS
dc.subjectPANEL DATA SETS
dc.subjectSOCIAL ASSISTANCE
dc.subjectREGIONS
dc.subjectSTATISTICS
dc.subjectEVALUATION
dc.subjectSIGNIFICANCE LEVEL
dc.subjectPOOR HOUSEHOLDS
dc.subjectSAMPLING
dc.subjectRURAL AREAS
dc.subjectPOVERTY
dc.subjectPOOR HOUSEHOLD
dc.subjectHOUSEHOLD HEAD
dc.subjectPANEL DATA SETS
dc.subjectCONSUMPTION EXPENDITURES
dc.subjectSIGNIFICANCE LEVEL
dc.subjectNATIONAL POVERTY
dc.subjectHOUSEHOLD CONSUMPTION
dc.subjectECONOMETRICS
dc.subjectSTANDARD ERRORS
dc.subjectCONSUMPTION DATA
dc.subjectPOVERTY STATUS
dc.subjectPOVERTY RATE
dc.subjectPOOR
dc.subjectPREDICTION
dc.subjectPOVERTY ASSESSMENT
dc.subjectCONSUMPTION EXPENDITURE
dc.subjectHOUSEHOLD SURVEYS
dc.subjectLEARNING
dc.subjectINDICATORS
dc.subjectRESEARCH
dc.subjectCONSUMPTION POVERTY
dc.subjectWELFARE INDICATORS
dc.subjectOUTCOMES
dc.subjectSOCIAL INDICATORS
dc.subjectPOVERTY STATUS
dc.subjectLINEAR REGRESSION
dc.subjectMISSING OBSERVATIONS
dc.subjectINEQUALITY
dc.subjectPOOR HOUSEHOLDS
dc.titleIs Random Forest a Superior Methodology for Predicting Poverty?en
dc.title.subtitleAn Empirical Assessmenten
dc.typeWorking Paperen
dc.typeDocument de travailfr
dc.typeDocumento de trabajoes
dspace.entity.typePublication
okr.crossref.titleIs Random Forest a Superior Methodology for Predicting Poverty? An Empirical Assessment
okr.date.disclosure2016-03-18
okr.doctypePublications & Research
okr.doctypePublications & Research::Policy Research Working Paper
okr.docurlhttp://documents.worldbank.org/curated/en/2016/03/26089791/random-forest-superior-methodology-predicting-poverty-empirical-assessment
okr.guid777401467987858907
okr.identifier.doi10.1596/1813-9450-7612
okr.identifier.doihttps://doi.org/10.1596/1813-9450-7612
okr.identifier.externaldocumentum090224b084212d02_1_0
okr.identifier.internaldocumentum26089791
okr.identifier.reportWPS7612
okr.importedtrue
okr.language.supporteden
okr.pdfurlhttp://www-wds.worldbank.org/external/default/WDSContentServer/WDSP/IB/2016/03/18/090224b084212d02/1_0/Rendered/PDF/Is0random0fore0empirical0assessment.pdfen
okr.topicPoverty Reduction::Poverty Monitoring & Analysis
okr.topicMacroeconomics and Economic Growth::Economic Theory & Research
okr.topicPoverty Reduction::Pro-Poor Growth
okr.topicPoverty Reduction::Achieving Shared Growth
okr.unitPoverty and Equity Global Practice Group
relation.isSeriesOfPublication26e071dc-b0bf-409c-b982-df2970295c87
relation.isSeriesOfPublication.latestForDiscovery26e071dc-b0bf-409c-b982-df2970295c87
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