IDEAS home Printed from https://ideas.repec.org/p/wpa/wuwpem/0203003.html
   My bibliography  Save this paper

Robust Estimation in Nonlinear Regression and Limited Dependent Variable Models

Author

Listed:
  • Pavel Cizek

    (Humboldt University ; CERGE-EI)

Abstract

Classical parametric estimation methods applied to nonlinear regression and limited-dependent-variable models are very sensitive to misspecification and data errors. This sensitivity is addressed by the theory of robust statistics which builds upon parametric specification, but provides methodology for designing misspecification-proof estimators by allowing for various "departures" of subsets of the data. However, this concept, developed in statistics, has so far been applied almost exclusively to linear regression models. Therefore, I adapt some robust methods, such as least trimmed squares, to nonlinear and limited- dependent-variable models. This paper presents the adapted robust estimators and proofs of their consistency. I also discuss several important examples of regression models which the proposed estimators can be applied to as well as suitable computational methods.

Suggested Citation

  • Pavel Cizek, 2002. "Robust Estimation in Nonlinear Regression and Limited Dependent Variable Models," Econometrics 0203003, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpem:0203003
    Note: Type of Document - Acrobat PDF; pages: 83
    as

    Download full text from publisher

    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/em/papers/0203/0203003.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    More about this item

    Keywords

    least trimmed squares; limited-dependent-variable-models; nonlinear regression; robust estimation;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:wpa:wuwpem:0203003. 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: EconWPA (email available below). General contact details of provider: https://econwpa.ub.uni-muenchen.de .

    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.