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Practical Correlation Bias Correction in Two-way Fixed Effects Linear Regression

Author

Listed:
  • Gaure, Simen

    (The Ragnar Frisch Centre for Economic Research, Oslo, Norway)

Abstract

When doing two-way fixed effects OLS estimations, both the variances and covariance of the fixed effects are biased. A formula for a bias correction is known, but in large datasets it involves inverses of impractically large matrices. We detail how to compute the bias correction in this case.

Suggested Citation

  • Gaure, Simen, 2014. "Practical Correlation Bias Correction in Two-way Fixed Effects Linear Regression," Memorandum 21/2014, Oslo University, Department of Economics.
    • Handle: RePEc:hhs:osloec:2014_021
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    File URL: http://www.sv.uio.no/econ/english/research/unpublished-works/working-papers/pdf-files/2014/memo-21-2014.pdf
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    References listed on IDEAS

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    1. David Card & Jörg Heining & Patrick Kline, 2013. "Workplace Heterogeneity and the Rise of West German Wage Inequality," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 128(3), pages 967-1015.
    2. Davidson, Carl & Heyman, Fredrik & Matusz, Steven & Sjöholm, Fredrik & Zhu, Susan Chun, 2014. "Globalization and imperfect labor market sorting," Journal of International Economics, Elsevier, vol. 94(2), pages 177-194.
    3. John M. Abowd & Francis Kramarz & David N. Margolis, 1999. "High Wage Workers and High Wage Firms," Econometrica, Econometric Society, vol. 67(2), pages 251-334, March.
    4. Cristian Bartolucci & Francesco Devicienti, 2012. "Better Workers Move to Better Firms: A Simple Test to Identify Sorting," Carlo Alberto Notebooks 259, Collegio Carlo Alberto.
    5. Mendes, Rute & van den Berg, Gerard J. & Lindeboom, Maarten, 2010. "An empirical assessment of assortative matching in the labor market," Labour Economics, Elsevier, vol. 17(6), pages 919-929, December.
    6. Johannes F. Schmieder, 2009. "GPREG: Stata module to estimate regressions with two dimensional fixed effects," Statistical Software Components S457048, Boston College Department of Economics.
    7. John R. Graham & Si Li & Jiaping Qiu, 2012. "Managerial Attributes and Executive Compensation," The Review of Financial Studies, Society for Financial Studies, vol. 25(1), pages 144-186.
    8. M. J. Andrews & L. Gill & T. Schank & R. Upward, 2008. "High wage workers and low wage firms: negative assortative matching or limited mobility bias?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(3), pages 673-697, June.
    9. Amine Ouazad, 2008. "A2REG: Stata module to estimate models with two fixed effects," Statistical Software Components S456942, Boston College Department of Economics.
    10. Anabela Carneiro & Paulo Guimarães & Pedro Portugal, 2012. "Real Wages and the Business Cycle: Accounting for Worker, Firm, and Job Title Heterogeneity," American Economic Journal: Macroeconomics, American Economic Association, vol. 4(2), pages 133-152, April.
    11. John M. Abowd & Robert H. Creecy & Francis Kramarz, 2002. "Computing Person and Firm Effects Using Linked Longitudinal Employer-Employee Data," Longitudinal Employer-Household Dynamics Technical Papers 2002-06, Center for Economic Studies, U.S. Census Bureau.
    12. Torben Sørensen & Rune Vejlin, 2013. "The importance of worker, firm and match effects in the formation of wages," Empirical Economics, Springer, vol. 45(1), pages 435-464, August.
    13. Gaure, Simen, 2013. "OLS with multiple high dimensional category variables," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 8-18.
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    Cited by:

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    2. Markussen, Simen & Røed, Knut, 2017. "The market for paid sick leave," Journal of Health Economics, Elsevier, vol. 55(C), pages 244-261.
    3. Paul Anand & Jere R. Behrman & Hai-Anh H. Dang & Sam Jones, 2018. "Inequality of opportunity in education: Accounting for the contributions of Sibs, schools and sorting across East Africa," Working Papers 480, ECINEQ, Society for the Study of Economic Inequality.
    4. David Card & Ana Rute Cardoso & Joerg Heining & Patrick Kline, 2018. "Firms and Labor Market Inequality: Evidence and Some Theory," Journal of Labor Economics, University of Chicago Press, vol. 36(S1), pages 13-70.
    5. Stephane Bonhomme, 2021. "Teams: Heterogeneity, Sorting, and Complementarity," Papers 2102.01802, arXiv.org.
    6. Paul Anand & Jere R. Behrman & Hai‐Anh H. Dang & Sam Jones, 2019. "Does sorting matter for learning inequality?: Evidence from East Africa," WIDER Working Paper Series wp-2019-110, World Institute for Development Economic Research (UNU-WIDER).
    7. Stéphane Bonhomme & Kerstin Holzheu & Thibaut Lamadon & Elena Manresa & Magne Mogstad & Bradley Setzler, 2023. "How Much Should We Trust Estimates of Firm Effects and Worker Sorting?," Journal of Labor Economics, University of Chicago Press, vol. 41(2), pages 291-322.
    8. Stéphane Bonhomme, 2021. "Selection on Welfare Gains: Experimental Evidence from Electricity Plan Choice," Working Papers 2021-15, Becker Friedman Institute for Research In Economics.

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    More about this item

    Keywords

    Limited mobility bias; Two way fuxed effects; Linear regression;
    All these keywords.

    JEL classification:

    • A19 - General Economics and Teaching - - General Economics - - - Other
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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