Economic Measurement Lost in a Random Forest? A Case Study of Employment Data.
Big data and machine learning (ML) offer transformative potential for economic measurement. This study evaluates the use of alternative employment data from a payroll processor to improve on timely measures of regional employment estimates, comparing ML methods--Lasso regression and Random Forest (R...
| Publicado en: | AEA Papers & Proceedings Vol. 115; pp. 68 - 73 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
| Publicado: |
American Economic Association
May2025
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=185592663&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 185592663 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 25740768 LNGQ jtl: AEA Papers & Proceedings issn: 25740768 maglogo: N pubinfo: dt: May2025 vid: 115 pid: 22 pub: American Economic Association artinfo: ui: 185592663 10.1257/pandp.20251103 ppf: 68 ppct: 5 formats: tig: atl: Economic Measurement Lost in a Random Forest? A Case Study of Employment Data. aug: au: Dunn, Abe English, Eric Hood, Kyle Mason, Lowell Quistorff, Brian affil: Bureau of Economic Analysis US Census Bureau Bureau of Labor Statistics su: Machine learning Random forest algorithms Big data Sampling errors Economic statistics Employment statistics Model validation sug: subj: Machine learning Random forest algorithms Big data Sampling errors Economic statistics Employment statistics Model validation ab: Big data and machine learning (ML) offer transformative potential for economic measurement. This study evaluates the use of alternative employment data from a payroll processor to improve on timely measures of regional employment estimates, comparing ML methods--Lasso regression and Random Forest (RF)--to linear models. RF models show substantial improvements in cross-validation but struggle with extrapolation, particularly during the pandemic. At the county level, greater data variation aids prediction, though sampling errors complicate performance. These findings highlight ML's promise in improving economic statistics while emphasizing the need for careful model selection, robust evaluation metrics, and consideration of data-specific challenges. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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