IDEAS home Printed from
   My bibliography  Save this paper

The GMM Estimation with Long Difference and Multiple Difference Operators



This paper proposes a new class of GMM estimators to increase the effciency of the coeffcient estimate relative to the ordinary least squares (OLS) estimator when all the error term and regressors having nonparametric autocorrelation. This class of GMM estimators are built on the moments generated from the long difference (LD) operator of Griliches and Hausman (1986) and those from the multiple difference (MD) operator of Tsay (2007). Most importantly, the GMM estimator is designed to beat both OLS and first-differenced (FD) estimators when neither OLS nor FD estimator attains Gauss-Markov bound in that the proposed method merges the information inherent in the moments of the OLS estimator and those of the FD one. Thus, the GMM estimator also resolves the dilemma concerning `to difference or not to difference' in the time series literature, because both level and differenced data are employed for the GMM estimation. The Monte Carlo experiments confirm the theoretical findings by showing that the GMM method has very good finite-sample power performance relative to both OLS and FD estimators.

Suggested Citation

  • Biing-Shen Kuo & Wen-Jen Tsay, 2008. "The GMM Estimation with Long Difference and Multiple Difference Operators," IEAS Working Paper : academic research 08-A002, Institute of Economics, Academia Sinica, Taipei, Taiwan.
  • Handle: RePEc:sin:wpaper:08-a002

    Download full text from publisher

    File URL:
    Download Restriction: no


    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:sin:wpaper:08-a002. 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: (HsiaoyunLiu). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.