IDEAS home Printed from https://ideas.repec.org/a/taf/applec/v52y2020i47p5129-5138.html
   My bibliography  Save this article

The natural rate of interest: information derived from a shadow rate model

Author

Listed:
  • Viktors Ajevskis

Abstract

The study proposes an estimation method of the natural rate of interest based on the shadow rate term structure of interest rates model and using information from nominal yields data. For the purpose of comparison and robustness check, different samples for the estimation of the natural rate of interest – three for the euro area and two for the US – are considered. The estimates based on all considered samples show a downturn trend in the estimated natural rates of interest for the euro area. However, since the beginning of 2013, this downward trend has levelled off. Compared to the results obtained by affine models, the shadow rate model produces lower estimates of the natural rates of interest. In order to demonstrate the use of the natural rate of interest, we employ the estimated series of the natural rate of interest in the balance-approach version of the Taylor rule. The results imply that, at the end of the sample in July 2017, Taylor rule-suggested policy rates were in line with the actual ECB policy rates.

Suggested Citation

  • Viktors Ajevskis, 2020. "The natural rate of interest: information derived from a shadow rate model," Applied Economics, Taylor & Francis Journals, vol. 52(47), pages 5129-5138, October.
    • Handle: RePEc:taf:applec:v:52:y:2020:i:47:p:5129-5138
    DOI: 10.1080/00036846.2020.1757029
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00036846.2020.1757029
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00036846.2020.1757029?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 look for a different version below or search for a different version of it.

    Other versions of this item:

    Citations

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


    Cited by:

    1. Lucio Gobbi & Ronny Mazzocchi & Roberto Tamborini, 2022. "Monetary policy, rational confidence, and Neo‐Fisherian depressions," Metroeconomica, Wiley Blackwell, vol. 73(4), pages 1179-1199, November.
    2. Brand, Claus & Goy, Gavin W & Lemke, Wolfgang, 2020. "Natural rate chimera and bond pricing reality," VfS Annual Conference 2020 (Virtual Conference): Gender Economics 224546, Verein für Socialpolitik / German Economic Association.
    3. Brand, Claus & Bielecki, Marcin & Penalver, Adrian, 2018. "The natural rate of interest: estimates, drivers, and challenges to monetary policy JEL Classification: E52, E43," Occasional Paper Series 217, European Central Bank.
    4. Christina Anderl & Guglielmo Maria Caporale, 2023. "Shadow rates as a measure of the monetary policy stance: Some international evidence," Scottish Journal of Political Economy, Scottish Economic Society, vol. 70(5), pages 399-422, November.
    5. Brand, Claus & Goy, Gavin W & Lemke, Wolfgang, 2020. "Natural rate chimera and bond pricing reality," VfS Annual Conference 2020 (Virtual Conference): Gender Economics 224546, Verein für Socialpolitik / German Economic Association.
    6. policy, Work stream on macroprudential & Albertazzi, Ugo & Martin, Alberto & Assouan, Emmanuelle & Tristani, Oreste & Galati, Gabriele & Vlassopoulos, Thomas, 2021. "The role of financial stability considerations in monetary policy and the interaction with macroprudential policy in the euro area," Occasional Paper Series 272, European Central Bank.

    More about this item

    JEL classification:

    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    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:52:y:2020:i:47:p:5129-5138. 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.