IDEAS home Printed from https://ideas.repec.org/p/wbk/wbrwps/9047.html
   My bibliography  Save this paper

New Approaches to the Identification of Low-Frequency Drivers : An Application to Technology Shocks

Author

Listed:
  • Dieppe,Alistair Matthew
  • Neville,Francis
  • Kindberg Hanlon,Gene Joseph

Abstract

This paper addresses the identification of low-frequency macroeconomic shocks, such as technology, in Structural Vector Autoregressions. Whilst identification issues with long-run restricted VARs are well documented, the recent attempt to overcome said issues using the Max-Share approach of Francis et al. (2014) and Barsky and Sims (2011) has its own shortcomings, primarily that they are vulnerable to bias from confounding non-technology shocks. A modification to the Max-Share approach and two further spectral methods are proposed to improve empirical identification. Performance directly hinges on whether these confounding shocks are of high or low frequency. Applied to US and emerging market data, spectral identifications are most robust across specifications, and non-technology shocks appear to be biasing traditional methods of identifying technology shocks. These findings also extend to the SVAR identification of dominant business-cycle shocks, which are shown will be a variance-weighted combination of shocks rather than a single structural driver.

Suggested Citation

  • Dieppe,Alistair Matthew & Neville,Francis & Kindberg Hanlon,Gene Joseph, 2019. "New Approaches to the Identification of Low-Frequency Drivers : An Application to Technology Shocks," Policy Research Working Paper Series 9047, The World Bank.
  • Handle: RePEc:wbk:wbrwps:9047
    as

    Download full text from publisher

    File URL: http://documents.worldbank.org/curated/en/133781571930814658/pdf/New-Approaches-to-the-Identification-of-Low-Frequency-Drivers-An-Application-to-Technology-Shocks.pdf
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Kindberg-Hanlon,Gene, 2021. "The Technology-Employment Trade-Off : Automation, Industry, and Income Effects," Policy Research Working Paper Series 9529, The World Bank.
    2. Dieppe, Alistair & Francis, Neville & Kindberg-Hanlon, Gene, 2021. "Technology and demand drivers of productivity dynamics in developed and emerging market economies," Working Paper Series 2533, European Central Bank.
    3. Dieppe, Alistair & Francis, Neville & Kindberg-Hanlon, Gene, 2021. "Technological and non-technological drivers of productivity dynamics in developed and emerging market economies," Journal of Economic Dynamics and Control, Elsevier, vol. 131(C).

    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:wbk:wbrwps:9047. 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: Roula I. Yazigi (email available below). General contact details of provider: https://edirc.repec.org/data/dvewbus.html .

    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.