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Técnicas Robustas y No Robustas para Identificar Outliers en el Análisis de Regresión

Author

Listed:
  • Darwin Ugarte Ontiveros

    (Universidad Privada Boliviana)

  • Ruth Marcela Aparicio de Guzmán

    (Universidad Privada Boliviana)

Abstract

Verificar si los resultados de un modelo de regresión reflejan el patrón de los datos, o si los mismos se deben a unas cuantas observaciones atípicas (outliers) es un paso importante en el proceso de investigación empírica. Para este propósito resulta aún común apoyarse en procedimientos (estándares) que no son eficaces para este propósito, al sufrir del denominado "masking effect", algunos de ellos sugeridos incluso en los libros tradicionales de econometría. El presente trabajo pretende alertar a la comunidad académica sobre el peligro de implementar estas técnicas estándares, mostrando el pésimo desempeño de las mismas. Asimismo, se sugiere aplicar otras técnicas más idóneas sugeridas en la literatura sobre "estadística robusta" para identificar outliers en el análisis multivariado. Para facilitar la aplicación de las mismas, el trabajo pone a disposición de la comunidad académica un programa en Stata del tipo do-file para identificar y categorizar outliers basado en el trabajo de [1]. Simulaciones de Monte Carlo dan evidencia de la aplicabilidad de la misma.

Suggested Citation

  • Darwin Ugarte Ontiveros & Ruth Marcela Aparicio de Guzmán, 2020. "Técnicas Robustas y No Robustas para Identificar Outliers en el Análisis de Regresión," Investigación & Desarrollo 0320, Universidad Privada Boliviana, revised Nov 2020.
    • Handle: RePEc:iad:wpaper:0320
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    References listed on IDEAS

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    1. Ruud, Paul A., 2000. "An Introduction to Classical Econometric Theory," OUP Catalogue, Oxford University Press, number 9780195111644, Decembrie.
    2. Vincenzo Verardi & Alice McCathie, 2012. "The S-estimator of multivariate location and scatter in Stata," Stata Journal, StataCorp LP, vol. 12(2), pages 299-307, June.
    3. Vincenzo Verardi & Marjorie Gassner & Darwin Ugarte Ontiveros, 2012. "Robustness for Dummies," Working Papers ECARES ECARES 2012-015, ULB -- Universite Libre de Bruxelles.
    4. Vincenzo Verardi & Christophe Croux, 2009. "Robust regression in Stata," Stata Journal, StataCorp LP, vol. 9(3), pages 439-453, September.
    5. Catherine Dehon & Marjorie Gassner & Vincenzo Verardi, 2009. "Beware of ‘Good’ Outliers and Overoptimistic Conclusions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(3), pages 437-452, June.
    6. Vincenzo Verardi & Catherine Vermandele, 2018. "Univariate and multivariate outlier identification for skewed or heavy-tailed distributions," Stata Journal, StataCorp LP, vol. 18(3), pages 517-532, September.
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    More about this item

    Keywords

    Outliers; Estadística Robusta; Análisis de Regresión; Stata.;
    All these keywords.

    JEL classification:

    • B23 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Econometrics; Quantitative and Mathematical Studies

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