[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
IDEAS home Printed from https://ideas.repec.org/p/sce/scecf1/150.html
   My bibliography  Save this paper

Nonparametric Likelihood Methods for Estimation and Inference in Moment Condition Models with Weak Instruments

Author

Listed:
  • Nikolay Gospodinov
Abstract
In this paper, I examine the properties of the class of generalized empirical likelihood estimators of moment-condition models. These nonparametric likelihood estimators satisfy exactly the moment conditions and automatically remove any bias due to a lack of centering. Moreover, the bias of the empirical likelihood estimator has been found (Newey and Smith (2000)) to be the same as that for the infeasible optimal GMM, where the coefficients of the optimal linear combinations do not have to be estimated. I examine the finite sample properties of these alternative estimators in the presence of weakly identified parameters and show their robustness. Most importantly, the proposed inference procedure does not involve explicit estimation of the variance-covariance matrix, which can be problematic, especially in small samples with dependent data. The confidence sets for the parameters of interest are constructed by inverting the criterion test, whose limiting chi-square distribution at the true values of the parameters is preserved in the presence of weak instruments.

Suggested Citation

  • Nikolay Gospodinov, 2001. "Nonparametric Likelihood Methods for Estimation and Inference in Moment Condition Models with Weak Instruments," Computing in Economics and Finance 2001 150, Society for Computational Economics.
    • Handle: RePEc:sce:scecf1:150
    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.

    More about this item

    Keywords

    empirical likelihood; moment conditions; GMM; weak instruments;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General

    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:sce:scecf1:150. 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: Christopher F. Baum (email available below). General contact details of provider: https://edirc.repec.org/data/sceeeea.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.