IDEAS home Printed from https://ideas.repec.org/p/cam/camdae/9802.html
   My bibliography  Save this paper

Bias Reduction in Estimating Long-run Relationships from Dynamic Heterogenous Panels

Author

Abstract

This paper considers the small sample properties of the mean group estimator of the long-run coefficients in dynamic heterogeneous panels, and using Monte Carlo techniques examines the effectiveness of a number of alternative bias-correction procedures in reducing the small sample bias of these estimates. Four different bias-corrected estimators of the long-run coefficients are considered. A `naive' procedure which attempts to bias-correct the estimator of the long-run coefficients, two variations of a direct approach which derives bias-corrections of the estimators of the short-run coefficients, and a bootstrap bias- correction procedure.

Suggested Citation

  • Pesaran, M. H. & Zhao, Z., 1998. "Bias Reduction in Estimating Long-run Relationships from Dynamic Heterogenous Panels," Cambridge Working Papers in Economics 9802, Faculty of Economics, University of Cambridge.
    • Handle: RePEc:cam:camdae:9802
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below 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.

    Citations

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


    Cited by:

    1. Maurice J. G. Bun, 2003. "Bias Correction in the Dynamic Panel Data Model with a Nonscalar Disturbance Covariance Matrix," Econometric Reviews, Taylor & Francis Journals, vol. 22(1), pages 29-58, February.
    2. Andrew Hallett & Gert Peersman & Laura Piscitelli, 2004. "Investment Under Monetary Uncertainty: A Panel Data Investigation," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 31(2), pages 137-162, June.
    3. Yongcheol Shin & Ron P Smith & Mohammad Hashem Pesaran, 1998. "Pooled Mean Group Estimation of Dynamic Heterogeneous Panels," Edinburgh School of Economics Discussion Paper Series 16, Edinburgh School of Economics, University of Edinburgh.
    4. Andrew Hughes Hallett & Gert Peersman & Laura Piscitelli, 2004. "Investment Under Monetary Uncertainty: A Panel Investigation," Vanderbilt University Department of Economics Working Papers 0406, Vanderbilt University Department of Economics.

    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:cam:camdae:9802. 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: Jake Dyer (email available below). General contact details of provider: https://www.econ.cam.ac.uk/ .

    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.