IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2009.14282.html
   My bibliography  Save this paper

Predicting Non Farm Employment

Author

Listed:
  • 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

Suggested Citation

  • Tarun Bhatia, 2020. "Predicting Non Farm Employment," Papers 2009.14282, arXiv.org.
    • Handle: RePEc:arx:papers:2009.14282
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2009.14282
    File Function: Latest version
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2009.14282. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.