Publication:
Machine Learning Imputation of High Frequency Price Surveys in Papua New Guinea

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dc.contributor.authorAndrée, Bo Pieter Johannes
dc.contributor.authorPape, Utz Johann
dc.contributor.authorAndree, Bo, Pieter Johannes
dc.date.accessioned2023-09-28T16:47:08Z
dc.date.available2023-09-28T16:47:08Z
dc.date.issued2023-09-28
dc.description.abstractCapabilities to track fast-moving economic developments re-main limited in many regions of the developing world. This complicates prioritizing policies aimed at supporting vulnerable populations. To gain insight into the evolution of fluid events in a data scarce context, this paper explores the ability of recent machine-learning advances to produce continuous data in near-real-time by imputing multiple entries in ongoing surveys. The paper attempts to track inflation in fresh produce prices at the local market level in Papua New Guinea, relying only on incomplete and intermittent survey data. This application is made challenging by high intra-month price volatility, low cross-market price correlations, and weak price trends. The modeling approach uses chained equations to produce an ensemble prediction for multiple price quotes simultaneously. The paper runs cross-validation of the prediction strategy under different designs in terms of markets, foods, and time periods covered. The results show that when the survey is well-designed, imputations can achieve accuracy that is attractive when compared to costly–and logistically often infeasible–direct measurement. The methods have wider applicability and could help to fill crucial data gaps in data scarce regions such as the Pacific Islands, especially in conjunction with specifically designed continuous surveys.en
dc.identifierhttp://documents.worldbank.org/curated/en/099230409052324201/IDU05a856a9201920042e20b9fb0f2f29afbb088
dc.identifier.doi10.1596/1813-9450-10559
dc.identifier.urihttps://openknowledge.worldbank.org/handle/10986/40410
dc.languageEnglish
dc.language.isoen
dc.publisherWorld Bank, Washington, DC
dc.relation.ispartofseriesPolicy Research Working Papers; 10559
dc.rightsCC BY 3.0 IGO
dc.rights.holderWorld Bank
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/igo/
dc.subjectINFLATION
dc.subjectAGRICULTURE AND FOOD SECURITY
dc.subjectFOOD PRICES
dc.subjectECONOMIC SHOCKS
dc.subjectMACROECONOMIC MONITORING
dc.subjectMACHINE LEARNING ADVANCES
dc.titleMachine Learning Imputation of High Frequency Price Surveys in Papua New Guineaen
dc.typeWorking Paper
dspace.entity.typePublication
okr.crossref.titleMachine Learning Imputation of High Frequency Price Surveys in Papua New Guinea
okr.date.disclosure2023-09-05
okr.date.lastmodified2023-09-05T00:00:00Zen
okr.doctypePolicy Research Working Paper
okr.doctypePublications & Research
okr.docurlhttp://documents.worldbank.org/curated/en/099230409052324201/IDU05a856a9201920042e20b9fb0f2f29afbb088
okr.guid099230409052324201
okr.identifier.docmidIDU-5a856a92-1920-42e2-b9fb-f2f29afbb088
okr.identifier.doi10.1596/1813-9450-10559
okr.identifier.doihttp://dx.doi.org/10.1596/1813-9450-10559
okr.identifier.externaldocumentum34156147
okr.identifier.internaldocumentum34156147
okr.identifier.reportWPS10559
okr.import.id1891
okr.importedtrueen
okr.language.supporteden
okr.pdfurlhttp://documents.worldbank.org/curated/en/099230409052324201/pdf/IDU05a856a9201920042e20b9fb0f2f29afbb088.pdfen
okr.region.countryPapua New Guinea
okr.topicPoverty Reduction::Poverty Monitoring & Analysis
okr.topicAgriculture::Food Markets
okr.topicMacroeconomics and Economic Growth::Economic Theory & Research
okr.unitAgriculture and Food GE (SAGGL)
okr.unitData Analytics and Tools (DECAT)
okr.unitEFI-AFR2-POV-Poverty and Equity (EAWPV)
relation.isAuthorOfPublicationb5186440-89c7-472c-895e-d57cb74dfc1a
relation.isAuthorOfPublication.latestForDiscoveryb5186440-89c7-472c-895e-d57cb74dfc1a
relation.isSeriesOfPublication26e071dc-b0bf-409c-b982-df2970295c87
relation.isSeriesOfPublication.latestForDiscovery26e071dc-b0bf-409c-b982-df2970295c87
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