[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/fip/fedcwq/87580.html
   My bibliography  Save this paper

A New Tool for Robust Estimation and Identification of Unusual Data Points

Author

Listed:
  • Christian Garciga
  • Randal J. Verbrugge
Abstract
Most consistent estimators are what Müller (2007) terms “highly fragile”: prone to total breakdown in the presence of a handful of unusual data points. This compromises inference. Robust estimation is a (seldom-used) solution, but commonly used methods have drawbacks. In this paper, building on methods that are relatively unknown in economics, we provide a new tool for robust estimates of mean and covariance, useful both for robust estimation and for detection of unusual data points. It is relatively fast and useful for large data sets. Our performance testing indicates that our baseline method performs on par with, or better than, two of the currently best available methods, and that it works well on benchmark data sets. We also demonstrate that the issues we discuss are not merely hypothetical, by re-examining a prominent economic study and demonstrating its central results are driven by a set of unusual points.

Suggested Citation

  • Christian Garciga & Randal J. Verbrugge, 2020. "A New Tool for Robust Estimation and Identification of Unusual Data Points," Working Papers 20-08, Federal Reserve Bank of Cleveland.
    • Handle: RePEc:fip:fedcwq:87580
    DOI: 10.26509/frbc-wp-202008
    as

    Download full text from publisher

    File URL: https://doi.org/10.26509/frbc-wp-202008
    File Function: Full Text
    Download Restriction: no
    File URL: https://libkey.io/10.26509/frbc-wp-202008?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Card, David & Krueger, Alan B, 1994. "Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania," American Economic Review, American Economic Association, vol. 84(4), pages 772-793, September.
    2. William Wascher & David Neumark, 2000. "Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania: Comment," American Economic Review, American Economic Association, vol. 90(5), pages 1362-1396, December.
    3. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    4. Torti, Francesca & Perrotta, Domenico & Atkinson, Anthony C. & Riani, Marco, 2012. "Benchmark testing of algorithms for very robust regression: FS, LMS and LTS," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2501-2512.
    5. Muler, Nora & Yohai, V´ictor J., 2013. "Robust estimation for vector autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 65(C), pages 68-79.
    6. Croux, Christophe & Haesbroeck, Gentiane, 1999. "Influence Function and Efficiency of the Minimum Covariance Determinant Scatter Matrix Estimator," Journal of Multivariate Analysis, Elsevier, vol. 71(2), pages 161-190, November.
    7. Cerioli, Andrea, 2010. "Multivariate Outlier Detection With High-Breakdown Estimators," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 147-156.
    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. Garciga, Christian & Verbrugge, Randal, 2021. "Robust covariance matrix estimation and identification of unusual data points: New tools," Research in Economics, Elsevier, vol. 75(2), pages 176-202.
    2. Derk Bienen, 2002. "Mindestlohnreformen in Südamerika – ökonomische Rechtfertigung und praktische Umsetzung," Ibero America Institute for Econ. Research (IAI) Discussion Papers 090, Ibero-America Institute for Economic Research.
    3. Maria Marimpi & Pierre Koning, 2018. "Youth minimum wages and youth employment," IZA Journal of Labor Policy, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 7(1), pages 1-18, December.
    4. Mark B. Stewart, 2004. "The Impact of the Introduction of the U.K. Minimum Wage on the Employment Probabilities of Low-Wage Workers," Journal of the European Economic Association, MIT Press, vol. 2(1), pages 67-97, March.
    5. Lemos Sara, 2005. "Political Variables as Instruments for the Minimum Wage," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 4(1), pages 1-31, December.
    6. Shirley, Peter, 2018. "The response of commuting patterns to cross-border policy differentials: Evidence from the American Community Survey," Regional Science and Urban Economics, Elsevier, vol. 73(C), pages 1-16.
    7. Hyslop, Dean & Stillman, Steven, 2007. "Youth minimum wage reform and the labour market in New Zealand," Labour Economics, Elsevier, vol. 14(2), pages 201-230, April.
    8. Galán Sofía & Puente Sergio, 2015. "Minimum Wages: Do They Really Hurt Young People?," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 15(1), pages 299-328, January.
    9. Goto, Hideaki, 2008. "Labor Market Competitiveness and Poverty," Working Papers 51159, Cornell University, Department of Applied Economics and Management.
    10. Kragl, Jenny & Schöttner, Anja, 2011. "Wage floors and optimal job design," Bonn Econ Discussion Papers 01/2011, University of Bonn, Bonn Graduate School of Economics (BGSE).
    11. Werner, Thomas & Sell, Friedrich L. & Reinisch, David C., 2013. "Price effects of minimum wages: Evidence from the construction sector in East and West Germany," Working Papers in Economics 2013,4, Bundeswehr University Munich, Economic Research Group.
    12. Yonezawa, Koichi & Gómez, Miguel I. & McLaughlin, Edward W., 2022. "Impacts of Minimum Wage Increases in the US Retail Sector: Full-Time versus Part-Time Employment," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 47(2), May.
    13. Torberg Falch, 2003. "Estimating the Elasticity of Labour Supply to an Enterprise Utilizing a Quasi–Natural Experiment," Working Paper Series 3303, Department of Economics, Norwegian University of Science and Technology.
    14. Denis Fougere & Erwan Gautier & Herve Le Bihan, 2010. "Restaurant Prices and the Minimum Wage," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(7), pages 1199-1234, October.
    15. Pokojovy, Michael & Jobe, J. Marcus, 2022. "A robust deterministic affine-equivariant algorithm for multivariate location and scatter," Computational Statistics & Data Analysis, Elsevier, vol. 172(C).
    16. William E. Even & David A. Macpherson, 2014. "The Effect of the Tipped Minimum Wage on Employees in the U.S. Restaurant Industry," Southern Economic Journal, John Wiley & Sons, vol. 80(3), pages 633-655, January.
    17. Price V. Fishback & Andrew J. Seltzer, 2021. "The Rise of American Minimum Wages, 1912–1968," Journal of Economic Perspectives, American Economic Association, vol. 35(1), pages 73-96, Winter.
    18. Möller, Joachim & König, Marion, 2008. "Mindestlohneffekte des Entsendegesetzes? : eine Mikrodatenanalyse für die deutsche Bauwirtschaft (Minimum wage effects of the worker posting law? : a micro data analysis for the German construction se," Zeitschrift für ArbeitsmarktForschung - Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 41(2/3), pages 327-346.
    19. Mark B. Stewart & Joanna K. Swaffield, 2008. "The Other Margin: Do Minimum Wages Cause Working Hours Adjustments for Low‐Wage Workers?," Economica, London School of Economics and Political Science, vol. 75(297), pages 148-167, February.
    20. Arismendi, Juan C. & Broda, Simon, 2017. "Multivariate elliptical truncated moments," Journal of Multivariate Analysis, Elsevier, vol. 157(C), pages 29-44.

    More about this item

    Keywords

    big data; machine learning; robust estimation; detMCD; RMVN; fragility; outlier identification;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

    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:fip:fedcwq:87580. 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: 4D Library (email available below). General contact details of provider: https://edirc.repec.org/data/frbclus.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.