IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/69064.html
   My bibliography  Save this paper

Estimation bias due to duplicated observations: a Monte Carlo simulation

Author

Listed:
  • Sarracino, Francesco
  • Mikucka, Malgorzata

Abstract

This paper assesses how duplicate records affect the results from regression analysis of survey data, and it compares the effectiveness of five solutions to minimize the risk of obtaining biased estimates. Results show that duplicate records create considerable risk of obtaining biased estimates. The chances of obtaining unbiased estimates in presence of a single sextuplet of identical observations is 41.6%. If the dataset contains about 10% of duplicated observations, then the probability of obtaining unbiased estimates reduces to nearly 11%. Weighting the duplicate cases by the inversion of their multiplicity minimizes the bias when multiple doublets are present in the data. Our results demonstrate the risks of using data in presence of non-unique observations and call for further research on strategies to analyze affected data.

Suggested Citation

  • Sarracino, Francesco & Mikucka, Malgorzata, 2016. "Estimation bias due to duplicated observations: a Monte Carlo simulation," MPRA Paper 69064, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:69064
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/69064/1/MPRA_paper_69064.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    duplicated observations; estimation bias; Monte Carlo simulation; inference;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

    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:pra:mprapa:69064. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.