IDEAS home Printed from https://ideas.repec.org/a/iad/wpaper/0320.html
   My bibliography  Save this article

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, Universidad Privada Boliviana, vol. 20(1), pages 41-56.
  • Handle: RePEc:iad:wpaper:0320
    as

    Download full text from publisher

    File URL: http://www1.upb.edu/RePEc/iad/wpaper/0320.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Vincenzo Verardi & Marjorie Gassner & Darwin Ugarte Ontiveros, 2012. "Robustness for Dummies," Working Papers ECARES ECARES 2012-015, ULB -- Universite Libre de Bruxelles.
    3. 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.
    4. 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.
    5. Ruud, Paul A., 2000. "An Introduction to Classical Econometric Theory," OUP Catalogue, Oxford University Press, number 9780195111644.
    6. Vincenzo Verardi & Christophe Croux, 2009. "Robust regression in Stata," Stata Journal, StataCorp LP, vol. 9(3), pages 439-453, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gustavo Canavire-Bacarreza & Luis Castro Peñarrieta & Darwin Ugarte Ontiveros, 2021. "Outliers in Semi-Parametric Estimation of Treatment Effects," Econometrics, MDPI, vol. 9(2), pages 1-32, April.
    2. Vincenzo Verardi & Joachim Wagner, 2021. "Productivity Premia for German Manufacturing Firms Exporting to the Euro-area and Beyond: First Evidence from Robust Fixed Effects Estimations," World Scientific Book Chapters, in: Joachim Wagner (ed.), MICROECONOMETRIC STUDIES OF FIRMS’ IMPORTS AND EXPORTS Advanced Methods of Analysis and Evidence from German Enterprises, chapter 7, pages 87-109, World Scientific Publishing Co. Pte. Ltd..
    3. Jan Ámos Víšek, 2015. "Estimating the Model with Fixed and Random Effects by a Robust Method," Methodology and Computing in Applied Probability, Springer, vol. 17(4), pages 999-1014, December.
    4. Wagner Joachim, 2013. "The Great Export Recovery in German Manufacturing Industries, 2009/2010," Review of Economics, De Gruyter, vol. 64(3), pages 325-340, December.
    5. Goetghebuer, Tatiana, 2011. "Productive inefficiency in patriarchal family farms: evidence from Mali," Proceedings of the German Development Economics Conference, Berlin 2011 34, Verein für Socialpolitik, Research Committee Development Economics.
    6. Verardi Vincenzo & Wagner Joachim, 2011. "Robust Estimation of Linear Fixed Effects Panel Data Models with an Application to the Exporter Productivity Premium," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(4), pages 546-557, August.
    7. Joachim Wagner & Yama Temouri, 2021. "Do Outliers and Unobserved Heterogeneity Explain the Exporter Productivity Premium? Evidence from France, Germany and the United Kingdom," World Scientific Book Chapters, in: Joachim Wagner (ed.), MICROECONOMETRIC STUDIES OF FIRMS’ IMPORTS AND EXPORTS Advanced Methods of Analysis and Evidence from German Enterprises, chapter 13, pages 223-236, World Scientific Publishing Co. Pte. Ltd..
    8. Biewen, Martin & Weiser, Constantin, 2011. "A New Approach to Testing Marginal Productivity Theory," IZA Discussion Papers 6113, Institute of Labor Economics (IZA).
    9. Badi H. Baltagi & Georges Bresson, 2012. "A Robust Hausman–Taylor Estimator," Advances in Econometrics, in: Essays in Honor of Jerry Hausman, pages 175-214, Emerald Group Publishing Limited.
    10. Wagner Joachim, 2014. "A Note on the Granular Nature of Imports in German Manufacturing Industries," Review of Economics, De Gruyter, vol. 65(3), pages 241-252, December.
    11. Kaffine, Daniel T. & Davis, Graham A., 2017. "A multi-row deletion diagnostic for influential observations in small-sample regressions," Computational Statistics & Data Analysis, Elsevier, vol. 108(C), pages 133-145.
    12. Wagner, Joachim, 2013. "The granular nature of the great export collapse in German manufacturing industries, 2008/2009," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 7, pages 1-21.
    13. Rodolphe Desbordes & Vincenzo Verardi, 2012. "A robust instrumental-variables estimator," Stata Journal, StataCorp LP, vol. 12(2), pages 169-181, June.
    14. Jan R. Magnus & Wendun Wang & Xinyu Zhang, 2016. "Weighted-Average Least Squares Prediction," Econometric Reviews, Taylor & Francis Journals, vol. 35(6), pages 1040-1074, June.
    15. Nathan H. Miller, 2008. "Competition When Consumers Value Firm Scope," EAG Discussions Papers 200807, Department of Justice, Antitrust Division.
    16. Wang, Jianzhou & Xiong, Shenghua, 2014. "A hybrid forecasting model based on outlier detection and fuzzy time series – A case study on Hainan wind farm of China," Energy, Elsevier, vol. 76(C), pages 526-541.
    17. Doko Tchatoka, Firmin Sabro, 2012. "Specification Tests with Weak and Invalid Instruments," MPRA Paper 40185, University Library of Munich, Germany.
    18. John M. Abowd & Francis Kramarz & Sébastien Pérez-Duarte & Ian M. Schmutte, 2018. "Sorting Between and Within Industries: A Testable Model of Assortative Matching," Annals of Economics and Statistics, GENES, issue 129, pages 1-32.
    19. Lullit Getachew & Robin C. Sickles, 2007. "The policy environment and relative price efficiency of Egyptian private sector manufacturing: 1987|88-1995|96," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(4), pages 703-728.
    20. Hugo Benítez-Silva & Debra Dwyer & Wayne-Roy Gayle & Thomas Muench, 2008. "Expectations in micro data: rationality revisited," Empirical Economics, Springer, vol. 34(2), pages 381-416, March.

    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

    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:iad:wpaper:0320. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Ricardo Nogales C. (email available below). General contact details of provider: https://edirc.repec.org/data/ciupbbo.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.