IDEAS home Printed from https://ideas.repec.org/a/erh/journl/v5y2013i2p43-52.html
   My bibliography  Save this article

Confirmation, Correction and Improvement for Outlier Validation Using Dummy Variables: t-Statistics or F-Incremental Statistics is not enough in OLS

Author

Listed:
  • Arzdar Kiraci

    (Siirt University, Faculty of Economics and Administrative Sciences.)

Abstract

Dummy variables can be used to detect, validate and measure the impact of outliers in data. This paper uses a model to evaluate the effectiveness of dummy variables in detecting outliers. While generally confirming some findings in the literature, the model refutes the presumption that the t˗statistic or the F-incremental statistic is enough to validate an observation as an outlier. In order to rectify this fallacy, this paper recommends an easily-calculable robust standardized residual statistic that is more compatible with the definition of outliers.The robust standardized residual statistic suggested herein is still used in many robust regression methods and is more effective than the t-statistic or the F-incremental statistic in validating outliers with dummy variables. The results of this study suggest some practical recommendations for dealing with outliers and improvements in maintaining the integrity of data. We recommend all previous studies using this statistics be revised in light of the findings presented in this paper.

Suggested Citation

  • Arzdar Kiraci, 2013. "Confirmation, Correction and Improvement for Outlier Validation Using Dummy Variables: t-Statistics or F-Incremental Statistics is not enough in OLS," International Econometric Review (IER), Econometric Research Association, vol. 5(2), pages 43-52, September.
    • Handle: RePEc:erh:journl:v:5:y:2013:i:2:p:43-52
    as

    Download full text from publisher

    File URL: http://www.era.org.tr/makaleler/23080056.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Frees,Edward W., 2004. "Longitudinal and Panel Data," Cambridge Books, Cambridge University Press, number 9780521828284, January.
    2. Zaman, Asad & Rousseeuw, Peter J. & Orhan, Mehmet, 2001. "Econometric applications of high-breakdown robust regression techniques," Economics Letters, Elsevier, vol. 71(1), pages 1-8, April.
    3. Frees,Edward W., 2004. "Longitudinal and Panel Data," Cambridge Books, Cambridge University Press, number 9780521535380, January.
    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. Kerstens, Kristiaan & Van de Woestyne, Ignace, 2014. "Comparing Malmquist and Hicks–Moorsteen productivity indices: Exploring the impact of unbalanced vs. balanced panel data," European Journal of Operational Research, Elsevier, vol. 233(3), pages 749-758.
    2. Naima Chrid & Sami Saafi & Mohamed Chakroun, 2021. "Export Upgrading and Economic Growth: a Panel Cointegration and Causality Analysis," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 12(2), pages 811-841, June.
    3. Patricio Aroca & Juan Gabriel Brida & Juan Sebastián Pereyra & Serena Volo, 2014. "Tourism statistics: correcting data inadequacy using coarsened exact matching," BEMPS - Bozen Economics & Management Paper Series BEMPS22, Faculty of Economics and Management at the Free University of Bozen.
    4. Fulvia Pennoni & Beata Bal-Domańska, 2022. "NEETs and Youth Unemployment: A Longitudinal Comparison Across European Countries," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 162(2), pages 739-761, July.
    5. Marco Angrisani & Kevin Foster & Marcin Hitczenko, 2015. "The 2013 Survey of Consumer Payment Choice: technical appendix," Research Data Report 15-5, Federal Reserve Bank of Boston.
    6. Rodrigo V. Ventura & Manoela Cabo & Rafael Caixeta & Elton Fernandes & Vicente Aprigliano Fernandes, 2020. "Air Transportation Income and Price Elasticities in Remote Areas: The Case of the Brazilian Amazon Region," Sustainability, MDPI, vol. 12(15), pages 1-18, July.
    7. Lynn, Peter & Bosch, Oriol, 2021. "Methodological lessons from the pilot longitudinal survey on debt advice," ISER Working Paper Series 2021-03, Institute for Social and Economic Research.
    8. Caudill, Jonathan W. & Getty, Ryan & Smith, Rick & Patten, Ryan & Trulson, Chad R., 2013. "Discouraging window breakers: The lagged effects of police activity on crime," Journal of Criminal Justice, Elsevier, vol. 41(1), pages 18-23.
    9. Adolfo Maza & Paula Gutiérrez-Portilla, 2022. "Outward FDI and exports relation: A heterogeneous panel approach dealing with cross-sectional dependence," International Economics, CEPII research center, issue 170, pages 174-189.
    10. Abayomi Oredegbe, 2022. "Competition and Banking Industry Stability: How Do BRICS and G7 Compare?," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 21(1), pages 7-31, March.
    11. Maliyamu Abudureheman & Qingzhe Jiang & Xiucheng Dong & Cong Dong, 2022. "CO 2 Emissions in China: Does the Energy Rebound Matter?," Energies, MDPI, vol. 15(12), pages 1-25, June.
    12. Edward Frees & Jee-Seon Kim, 2006. "Multilevel Model Prediction," Psychometrika, Springer;The Psychometric Society, vol. 71(1), pages 79-104, March.
    13. Jun Zhao & Xiucheng Dong & Kangyin Dong, 2021. "Can agglomeration of producer services reduce urban–rural income inequality? The case of China," Australian Economic Papers, Wiley Blackwell, vol. 60(4), pages 736-762, December.
    14. LU, Yi & Zhao, Jianting & Wu, Xueying & Lo, Siu Ming, 2020. "Escaping to nature in pandemic: a natural experiment of COVID-19 in Asian cities," SocArXiv rq8sn, Center for Open Science.
    15. Kaixing Huang, 2015. "The Economic Impacts of Global Warming on Agriculture: the Role of Adaptation," School of Economics and Public Policy Working Papers 2015-20, University of Adelaide, School of Economics and Public Policy.
    16. Torben Dall Schmidt & Peter Sandholt Jensen, 2012. "Social networks and regional recruitment of foreign labour: Firm recruitment methods and spatial sorting in Denmark," Papers in Regional Science, Wiley Blackwell, vol. 91(4), pages 795-821, November.
    17. Li, Jiaman & Dong, Kangyin & Taghizadeh-Hesary, Farhad & Wang, Kun, 2022. "3G in China: How green economic growth and green finance promote green energy?," Renewable Energy, Elsevier, vol. 200(C), pages 1327-1337.
    18. Alberto Aiolfi & Emanuele Asti & Emanuele Rausa & Giulia Bonavina & Gianluca Bonitta & Luigi Bonavina, 2018. "Use of C-reactive protein for the early prediction of anastomotic leak after esophagectomy: Systematic review and Bayesian meta-analysis," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-13, December.
    19. Jun Wang & Yang Wang & Shiji Wang, 2016. "Biophysical and socioeconomic drivers of the dynamics in snow hazard impacts across scales and over heterogeneous landscape in Northern Tibet," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(3), pages 1499-1514, April.
    20. Giulio Fusco, 2022. "Climate Change and Food Security in the Northern and Eastern African Regions: A Panel Data Analysis," Sustainability, MDPI, vol. 14(19), pages 1-10, October.

    More about this item

    Keywords

    Dummy Variable; t-Statistic; Outlier; Robust Dummy Statistic; Robust Standardized Residual;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

    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:erh:journl:v:5:y:2013:i:2:p:43-52. 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: M. F. Cosar (email available below). General contact details of provider: https://edirc.repec.org/data/eratrea.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.