IDEAS home Printed from https://ideas.repec.org/p/msh/ebswps/2001-7.html
   My bibliography  Save this paper

Capturing the Shape of Business Cycles with Nonlinear Autoregressive Leading Indicator Models

Author

Listed:
  • Athanasopoulos, G.
  • Anderson, H.M.
  • Vahid, F.

Abstract

This paper studies linear and linear autoregressive leading indicator models of business cycles in OECD countries. The models use the spread between short-term and long-term interest rates as leading indicators for GDP, and their success in capturing business cycles gauged by the non-parametric procedures developed by Harding and Pagan (2001). Our preliminary findings indicate that bivariate nonlinear models of output and the interest rate spread can successfully capture the shape of the business cycle. In particular, they can capture the features of recession and the deviation of the actual path of the cycles from a triangular approximation to this path, both characteristics that other models of GDP fail to reproduce.

Suggested Citation

  • Athanasopoulos, G. & Anderson, H.M. & Vahid, F., 2001. "Capturing the Shape of Business Cycles with Nonlinear Autoregressive Leading Indicator Models," Monash Econometrics and Business Statistics Working Papers 7/01, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2001-7
    as

    Download full text from publisher

    File URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2001/wp7-01.pdf
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Elalaoui, Aicha, 2014. "Identifying and characterizing the business cycle: the case of Morocco," MPRA Paper 56811, University Library of Munich, Germany, revised May 2014.
    2. Harding, Don & Pagan, Adrian, 2001. "Extracting, Using and Analysing Cyclical Information," MPRA Paper 15, University Library of Munich, Germany.

    More about this item

    Keywords

    Business Cycles; Leading Indicators; Nonlinear Models; Yield Spread;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

    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:msh:ebswps:2001-7. 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: Professor Xibin Zhang (email available below). General contact details of provider: https://edirc.repec.org/data/dxmonau.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.