Advanced Search
MyIDEAS: Login to save this paper or follow this series

Robust Estimation of GARMA Model Parameters and Application to Cointegration among Interest Rates of Industrialized Countries

Contents:

Author Info

  • Raji Ramachandran

    ()
    (Florida State University)

  • Paul Beaumont

    ()
    (Florida State University)

Abstract

Theoretical predictions for and empirical analysis of real economic time series indicate that there is dependence among distant observations. Empirical autocorrelation functions of such data show high persistence, decay slowly and show persistent cyclical patterns. Long-memory models take these stylized facts into account. The two long-memory models currently in vogue are Autoregressive Fractionally Integrated Moving Average process (ARFIMA) and Generalized Autoregressive Moving Average processes (GARMA). ARFIMA models take into account persistence in auto-correlations, whereas GARMA models characterize both the persistence and the cyclical patterns in the auto-correlation function. In the estimation of GARMA model parameters, the periodicity parameter, eta, converges faster than the others. This makes gradient-based methods unsuitable for estimating all parameters simultaneously. Chung (1996) and Gray et. al (1989) use a grid search on eta while estimating the other parameters by gradient methods. Here, we evaluate bisection techniques for eta combined with the Davidon-Fletcher-Powell gradient method for the other parameters. Based on these studies, we propose a robust and fast algorithm for GARMA model parameter estimation, including the difficult to recover parameter ranges. Such parameter ranges are of interest to economic data. As an application of our estimation procedure, we perform cointegration analysis on the interest rates of the industrialized countries by modeling them as GARMA processes. We look for evidence of cointegration among long-term interest rates of these countries. This study is motivated by the observed persistent, somewhat sinusoidal (rather than monotonic) decline of the autocorrelation function of the error correction term. We find that interest rates are better modelled as GARMA rather than ARFIMA processes and certain systems of interest rates are cointegrated. Using GARMA produces estimates with lower standard errors than other long-memory models. Further, we find that the difference between mean-reverting and nonstationary processes need not be considered in establishing cointegration. Rapid establishment of stationarity is useful in cointegration studies. Hence, we examined the power of the Dickie-Fuller test using generated GARMA data. We find that this test fails to identify stationarity correctly.

Download Info

To our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below under "Related research" 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 search for a similarly titled item that would be available.

Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 1999 with number 851.

as in new window
Length:
Date of creation: 01 Mar 1999
Date of revision:
Handle: RePEc:sce:scecf9:851

Contact details of provider:
Postal: CEF99, Boston College, Department of Economics, Chestnut Hill MA 02467 USA
Fax: +1-617-552-2308
Web page: http://fmwww.bc.edu/CEF99/
More information through EDIRC

Related research

Keywords:

References

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

Citations

Lists

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

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:sce:scecf9:851. 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: (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.