IDEAS home Printed from https://ideas.repec.org/p/ecm/wc2000/0892.html
   My bibliography  Save this paper

RMSE Reduction for GMM Estimators of Linear Time Series Models

Author

Listed:
  • Guido Kuersteiner

    (Massachusetts Institute of Technology)

Abstract

In this paper we analyze GMM estimators for time series models as advocated by Hayashi and Sims, and Hansen and Singleton. It is well known that these estimators achieve efficiency bounds if the number of lagged observations in the instrument set goes to infinity. A new version of the GMM estimator based on kernel weighted moment conditions is proposed. Higher order asymptotic expansions are used to obtain optimal rates of expansions for the number of instruments to minimize the asymptotic MSE of the estimator. Estimates of optimal bandwidth parameters are then used to construct a fully feasible GMM estimator where the number of lagged instruments are endogenously determined by the data. Expressions for the asymptotic bias of kernel weighted GMM estimators are obtained. It is shown that standard GMM procedures have larger asymptotic biases than kernel weighted GMM. A bias correction for the estimator is proposed. It is shown that the bias corrected version achieves a faster rate of convergence of the higher order terms of the MSE than the uncorrected estimator.

Suggested Citation

  • Guido Kuersteiner, 2000. "RMSE Reduction for GMM Estimators of Linear Time Series Models," Econometric Society World Congress 2000 Contributed Papers 0892, Econometric Society.
  • Handle: RePEc:ecm:wc2000:0892
    as

    Download full text from publisher

    File URL: http://fmwww.bc.edu/RePEc/es2000/0892.pdf
    File Function: main text
    Download Restriction: no

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mahmoud El-Gamal, 2001. "A Bayesian Interpretation Of Multiple Point Estimates," Econometric Reviews, Taylor & Francis Journals, vol. 20(2), pages 235-245.
    2. Hahn, Jinyong & Hausman, Jerry & Kuersteiner, Guido, 2007. "Long difference instrumental variables estimation for dynamic panel models with fixed effects," Journal of Econometrics, Elsevier, vol. 140(2), pages 574-617, October.
    3. Nour Meddahi, 2003. "ARMA representation of integrated and realized variances," Econometrics Journal, Royal Economic Society, vol. 6(2), pages 335-356, December.
    4. Xu Cheng & Zhipeng Liao, 2012. "Select the Valid and Relevant Moments: A One-Step Procedure for GMM with Many Moments," PIER Working Paper Archive 12-045, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

    More about this item

    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:ecm:wc2000:0892. 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). General contact details of provider: http://edirc.repec.org/data/essssea.html .

    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.