IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this article

A test for improved multi-step forecasting

Listed author(s):
  • John Haywood
  • Granville Tunnicliffe Wilson
Registered author(s):

    We propose a general test of whether a time-series model, with parameters estimated by minimizing the single-step forecast error sum of squares, is robust with respect to multi-step prediction, for some specified lead time. The test may be applied to a, possibly seasonal, autoregressive integrated moving average (ARIMA) model using the parameters and residuals following maximum likelihood estimation. It is based on a score statistic, evaluated at these estimated parameters, which measures the sensitivity of the multi-step forecast error variance with respect to the parameters. We derive the large sample properties of the test and show by a simulation study that it has acceptable small sample size properties for higher lead times when applied to the integrated moving average or IMA model that gives rise to the exponentially weighted moving average predictor. We investigate the power of the test when the IMA(1,1) model has been fitted to an ARMA(1,1) process. Further, we demonstrate the high power of the test when an AR is fitted to a process generated as the sum of a stochastic trend and cycle plus noise. We use frequency domain methods for the derivation and sampling properties of the test, and to give insight into its application. The test is illustrated on two real series, and an R function for its general application is available from . Copyright 2009 Blackwell Publishing Ltd

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

    File URL:
    File Function: link to full text
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

    Article provided by Wiley Blackwell in its journal Journal of Time Series Analysis.

    Volume (Year): 30 (2009)
    Issue (Month): 6 (November)
    Pages: 682-707

    in new window

    Handle: RePEc:bla:jtsera:v:30:y:2009:i:6:p:682-707
    Contact details of provider: Web page:

    Order Information: Web:

    No references listed on IDEAS
    You can help add them by filling out this form.

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    When requesting a correction, please mention this item's handle: RePEc:bla:jtsera:v:30:y:2009:i:6:p:682-707. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley-Blackwell Digital Licensing)

    or (Christopher F. Baum)

    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.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with 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 profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.