IDEAS home Printed from https://ideas.repec.org/a/taf/applec/v56y2024i51p6300-6313.html
   My bibliography  Save this article

Nowcasting the nowcasting - Forecasting ISM Business surveys (PMI and NSI) with weekly Google trends

Author

Listed:
  • Joni Heikkinen
  • Kari Heimonen

Abstract

Changes in economic conditions can occur suddenly with drastic effects. However, economic statistics are published with significant lags, e.g. GDP, and more timely information about the economy is required. Nowcasting methods have become widely popular for providing up-to-date information about the current economic stance. This study adds a novel idea to the previous literature by nowcasting the nowcasting, i.e. the purchasing manager’s index (PMI) and the non-manufacturing survey index (NSI) of the ISM Business survey indicators with the weekly Google Trends data. We used two-dimension reduction methods: the principal component analysis (PCA) and partial least squares (PLS) to eliminate ‘the curse of dimensionality’. Pseudo-out-of-sample exercises performed with different Google Trends search categories indicated that Google Search data is able to generate useful information to nowcast the nowcasting. In particular, we contribute the existing literature that weekly Google Search data can nowcast the monthly PMI and NSI.

Suggested Citation

  • Joni Heikkinen & Kari Heimonen, 2024. "Nowcasting the nowcasting - Forecasting ISM Business surveys (PMI and NSI) with weekly Google trends," Applied Economics, Taylor & Francis Journals, vol. 56(51), pages 6300-6313, November.
    • Handle: RePEc:taf:applec:v:56:y:2024:i:51:p:6300-6313
    DOI: 10.1080/00036846.2023.2273235
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00036846.2023.2273235
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00036846.2023.2273235?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    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:taf:applec:v:56:y:2024:i:51:p:6300-6313. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEC20 .

    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.