Predicting Non Farm Employment

Tarun Bhatia

Papers from arXiv.org

Abstract: U.S. Nonfarm employment is considered one of the key indicators for assessing the state of the labor market. Considerable deviations from the expectations can cause market moving impacts. In this paper, the total U.S. nonfarm payroll employment is predicted before the release of the BLS employment report. The content herein outlines the process for extracting predictive features from the aggregated payroll data and training machine learning models to make accurate predictions. Publically available revised employment report by BLS is used as a benchmark. Trained models show excellent behaviour with R2 of 0.9985 and 99.99% directional accuracy on out of sample periods from January 2012 to March 2020. Keywords Machine Learning; Economic Indicators; Ensembling; Regression, Total Nonfarm Payroll

Date: 2020-09
New Economics Papers: this item is included in nep-big and nep-cmp
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2009.14282

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