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Predicting Non Farm Employment

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  • Tarun Bhatia
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

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  • Tarun Bhatia, 2020. "Predicting Non Farm Employment," Papers 2009.14282, arXiv.org.
    • Handle: RePEc:arx:papers:2009.14282
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    File URL: http://arxiv.org/pdf/2009.14282
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    Cited by:

    1. Salisu, Afees A. & Bouri, Elie & Gupta, Rangan, 2022. "Out-of-sample predictability of gold market volatility: The role of US Nonfarm Payroll," The Quarterly Review of Economics and Finance, Elsevier, vol. 86(C), pages 482-488.

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