IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-319-02499-8_21.html

Threshold Structures in Economic and Financial Time Series

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

Listed:
  • Marcella Niglio

    (Univerisità degli Studi di Salerno, Di.S.E.S.)

  • Cosimo Damiano Vitale

    (Univerisità degli Studi di Salerno, Di.S.E.S.)

Abstract

In this paper we present some nonlinear autoregressive moving average (NARMA) models proposed in the literature focusing then the attention on the Threshold ARMA (TARMA) model with exogenous threshold variable. The main features of this stochastic structure are shortly discussed and the forecasts generation is presented. It is widely known that in presence of most economic time series the nonlinearity of the data generating process can be well caught by the threshold model under analysis even if, at the same time, the forecast accuracy is not always equally encouraging. Starting from this statement we evaluate how the forecast accuracy of the US Consumer Price Index can be improved when a TARMA model with exogenous threshold variable is fitted to the data. We give empirical evidence that predictors based on a squared loss function can be more accurate when the spread between US Treasury Bonds and US Treasury Bills is selected as threshold variable.

Suggested Citation

  • Marcella Niglio & Cosimo Damiano Vitale, 2014. "Threshold Structures in Economic and Financial Time Series," Springer Books, in: Marco Corazza & Claudio Pizzi (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 231-241, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-02499-8_21
    DOI: 10.1007/978-3-319-02499-8_21
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:spr:sprchp:978-3-319-02499-8_21. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.