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Loss Functions for Detecting Outliers in Panel Data: An Introduction

Author

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  • Charles, Coleman

Abstract

Loss functions are introduced for detecting outliers in panel data. The loss functions for nonnegative data take into account both the size of the base and the relative change. When the data generation processes take a particular form, an exact parametrization is available. The loss functions are extended to variables whose outlier criteria depend on another variable and to data of mixed sign. In the latter case, the geometry dictates one parametrization.

Suggested Citation

  • Charles, Coleman, 2003. "Loss Functions for Detecting Outliers in Panel Data: An Introduction," MPRA Paper 77844, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:77844
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    File URL: https://mpra.ub.uni-muenchen.de/77844/1/MPRA_paper_77844.pdf
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    More about this item

    Keywords

    Estimates; forecasts; outliers; data quality; panel data;
    All these keywords.

    JEL classification:

    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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