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OLS with multiple high dimensional category variables

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  • Gaure, Simen

Abstract

A new algorithm is proposed for OLS estimation of linear models with multiple high-dimensional category variables. It is a generalization of the within transformation to arbitrary number of category variables. The approach, unlike other fast methods for solving such problems, provides a covariance matrix for the remaining coefficients. The article also sets out a method for solving the resulting sparse system, and the new scheme is shown, by some examples, to be comparable in computational efficiency to other fast methods. The method is also useful for transforming away groups of pure control dummies. A parallelized implementation of the proposed method has been made available as an R-package lfe on CRAN.

Suggested Citation

  • Gaure, Simen, 2013. "OLS with multiple high dimensional category variables," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 8-18.
  • Handle: RePEc:eee:csdana:v:66:y:2013:i:c:p:8-18
    DOI: 10.1016/j.csda.2013.03.024
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    Cited by:

    1. J. Blaum & c. Lelarge & M. Peters, 2017. "Firm Size and the Intensive Margin of Import Demand," Working papers 657, Banque de France.
    2. repec:eee:jhecon:v:55:y:2017:i:c:p:244-261 is not listed on IDEAS
    3. Simen Markussen & Knut Røed, 2015. "Social Insurance Networks," Journal of Human Resources, University of Wisconsin Press, vol. 50(4), pages 1081-1113.
    4. Nikolas Mittag, 2015. "A Simple Method to Estimate Large Fixed Effects Models Applied to Wage Determinants and Matching," CERGE-EI Working Papers wp532, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    5. Alan Fernihough & Kevin Hjortshøj O'Rourke, 2014. "Coal and the European Industrial Revolution," The Institute for International Integration Studies Discussion Paper Series iiisdp439, IIIS.
    6. Donna, Javier D. & Veramendi, Gregory F., 2018. "Gender Differences within the Firm: Evidence from Two Million Travelers," MPRA Paper 92834, University Library of Munich, Germany.
    7. Mittag, Nikolas, 2016. "A Simple Method to Estimate Large Fixed Effects Models Applied to Wage Determinants and Matching," IZA Discussion Papers 10447, Institute of Labor Economics (IZA).
    8. Agerton, Mark & Hartley, Peter R. & Medlock, Kenneth B. & Temzelides, Ted, 2017. "Employment impacts of upstream oil and gas investment in the United States," Energy Economics, Elsevier, vol. 62(C), pages 171-180.
    9. Rios-Avila, Fernando, 2015. "Feasible fitting of linear models with N fixed effects," Stata Journal, StataCorp LP, vol. 15(3).
    10. Christian Elleby & Wusheng Yu & Qian Yu, 2018. "The Chinese Export Displacement Effect Revisited," IFRO Working Paper 2018/02, University of Copenhagen, Department of Food and Resource Economics.
    11. Diana Lúcia Gonzaga da Silva & Carlos Roberto Azzoni, 2016. "Location and wages: the contribution of firm and worker effects in Brazil," Working Papers, Department of Economics 2016_41, University of São Paulo (FEA-USP).
    12. Blaum, Joaquin & Lelarge, Claire & Peters, Michael, 2019. "Firm size, quality bias and import demand," Journal of International Economics, Elsevier, vol. 120(C), pages 59-83.
    13. Markussen, Simen & Røed, Knut, 2017. "The market for paid sick leave," Journal of Health Economics, Elsevier, vol. 55(C), pages 244-261.
    14. Markussen, Simen & Røed, Knut, 2017. "The gender gap in entrepreneurship – The role of peer effects," Journal of Economic Behavior & Organization, Elsevier, vol. 134(C), pages 356-373.
    15. Filippo Bontadini, 2019. "Power and Export Sophistication in Buyer-Supplier Relationships: Insights from Colombian Customs Data," SPRU Working Paper Series 2019-11, SPRU - Science Policy Research Unit, University of Sussex Business School.

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