IDEAS home Printed from https://ideas.repec.org/p/ags/amstas/293130.html
   My bibliography  Save this paper

BIAS REDUCTION IN A DYNAMIC REGRESSION MODEL: A Comparison of Jackknifed and Bias Corrected Least Squares Estimators

Author

Listed:
  • Kiviet, Jan
  • Phillips, Garry

Abstract

Employing small—sigma asymptotics we approximate the small—sample bias of the ordinary least—squares (OLS) estimator of the full coefficient vector in a linear regression model which includes a one period lagged dependent variable and an arbitrary number of fixed regressors. This bias term is used to construct a corrected ordinary least—squares (COLS) estimator which is unbiased to 0( cr2) . We also consider another technique for bias reduction, viz. jackknifing, and we present a simple expression for the JOLS(m) estimator: the m — delete jackknifed OLS estimator. Then we compare • the accuracy of the 0( cr2) approximation to the bias and the efficiency of OLS, COLS and JOLS(m) in a Monte Carlo study of artificial but realistic models. It is found that the bias is extremely sensitive to the value of a and that COLS can reduce it considerably without undue loss of efficiency if the standard deviation of the OLS lagged dependent variable coefficient estimate has a moderate value.

Suggested Citation

  • Kiviet, Jan & Phillips, Garry, 1988. "BIAS REDUCTION IN A DYNAMIC REGRESSION MODEL: A Comparison of Jackknifed and Bias Corrected Least Squares Estimators," University of Amsterdam, Actuarial Science and Econometrics Archive 293130, University of Amsterdam, Faculty of Economics and Business.
    • Handle: RePEc:ags:amstas:293130
    DOI: 10.22004/ag.econ.293130
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/293130/files/amsterdam075.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.293130?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
    ---><---

    Other versions of this item:

    More about this item

    Keywords

    Research Methods/ Statistical Methods;

    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:ags:amstas:293130. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/feuvanl.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.