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Centering and reference groups for estimates of fixed effects: Modifications to felsdvreg

Author

Listed:
  • Kata Mihaly

    () (The RAND Corporation)

  • Daniel F. McCaffrey

    () (The RAND Corporation)

  • J. R. Lockwood

    () (The RAND Corporation)

  • Tim R. Sass

    () (Florida State University)

Abstract

Availability of large, multilevel longitudinal databases in various fields including labor economics (with workers and firms observed over time) and ed- ucation research (with students and teachers observed over time) has increased the application of panel-data models with multiple levels of fixed-effects. Existing software routines for fitting fixed-effects models were not designed for applications in which the primary interest is obtaining estimates of any of the fixed-effects parameters. Such routines typically report estimates of fixed effects relative to arbitrary holdout units. Contrasts to holdout units are not ideal in cases where the fixed-effects parameters are of interest because they can change capriciously, they do not correspond to the structural parameters that are typically of inter- est, and they are inappropriate for empirical Bayes (shrinkage) estimation. We develop an improved parameterization of fixed-effects models using sum-to-zero constraints that provides estimates of fixed effects relative to mean effects within well-defined reference groups (e.g., all firms of a given type or all teachers of a given grade) and provides standard errors for those estimates that are appropriate for shrinkage estimation. We implement our parameterization in a Stata routine called felsdvregdm by modifying the felsdvreg routine designed for fitting high- dimensional fixed-effects models. We demonstrate our routine with an example dataset from the Florida Education Data Warehouse. Copyright 2010 by StataCorp LP.

Suggested Citation

  • Kata Mihaly & Daniel F. McCaffrey & J. R. Lockwood & Tim R. Sass, 2010. "Centering and reference groups for estimates of fixed effects: Modifications to felsdvreg," Stata Journal, StataCorp LP, vol. 10(1), pages 82-103, March.
  • Handle: RePEc:tsj:stataj:v:10:y:2010:i:1:p:82-103
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    References listed on IDEAS

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    1. repec:spr:empeco:v:54:y:2018:i:2:d:10.1007_s00181-017-1259-9 is not listed on IDEAS
    2. Elizabeth Dhuey & Justin Smith, 2014. "How important are school principals in the production of student achievement?," Canadian Journal of Economics, Canadian Economics Association, vol. 47(2), pages 634-663, May.
    3. Sandra Cavaco & Patricia Crifo & Antoine Rebérioux & Gwenaël Roudaut, 2014. "Independent directors: less informed, but better selected? New evidence from a two-way director-firm fixed effect model," EconomiX Working Papers 2014-58, University of Paris Nanterre, EconomiX.
    4. Kevin C. Bastian & Gary T. Henry & Charles L. Thompson, 2013. "Incorporating Access to More Effective Teachers into Assessments of Educational Resource Equity," Education Finance and Policy, MIT Press, vol. 8(4), pages 560-580, October.
    5. Deepti Goel & Bidisha Barooah, 2018. "Drivers of Student Performance: Evidence from Higher Secondary Public Schools in Delhi," Working Papers id:12881, eSocialSciences.
    6. Deepti Goel & Bidisha Barooah, 2018. "Drivers of Student Performance: Evidence from Higher Secondary Public Schools in Delhi," Working papers 289, Centre for Development Economics, Delhi School of Economics.
    7. Sass, Tim R. & Hannaway, Jane & Xu, Zeyu & Figlio, David N. & Feng, Li, 2012. "Value added of teachers in high-poverty schools and lower poverty schools," Journal of Urban Economics, Elsevier, vol. 72(2), pages 104-122.
    8. repec:eee:jhecon:v:60:y:2018:i:c:p:142-164 is not listed on IDEAS
    9. Goel, Deepti & Barooah, Bidisha, 2018. "Drivers of Student Performance: Evidence from Higher Secondary Public Schools in Delhi," GLO Discussion Paper Series 231, Global Labor Organization (GLO).
    10. Goldhaber, Dan & Liddle, Stephanie & Theobald, Roddy, 2013. "The gateway to the profession: Assessing teacher preparation programs based on student achievement," Economics of Education Review, Elsevier, vol. 34(C), pages 29-44.
    11. Koedel, Cory & Mihaly, Kata & Rockoff, Jonah E., 2015. "Value-added modeling: A review," Economics of Education Review, Elsevier, vol. 47(C), pages 180-195.
    12. Carlson, Deven & Lavertu, Stéphane, 2016. "Charter school closure and student achievement: Evidence from Ohio," Journal of Urban Economics, Elsevier, vol. 95(C), pages 31-48.
    13. Elizabeth Dhuey & Justin Smith, 2018. "How school principals influence student learning," Empirical Economics, Springer, vol. 54(2), pages 851-882, March.
    14. Alexander Ahammer & Thomas Schober, 2017. "Exploring Variations in Healthcare Expenditures – What is the Role of Practice Styles?," Economics working papers 2017-05, Department of Economics, Johannes Kepler University Linz, Austria.
    15. Cavaco, Sandra & Crifo, Patricia & Rebérioux, Antoine & Roudaut, Gwenael, 2017. "Independent directors: Less informed but better selected than affiliated board members?," Journal of Corporate Finance, Elsevier, vol. 43(C), pages 106-121.
    16. Goel, Deepti & Barooah, Bidisha, 2018. "Drivers of Student Performance: Evidence from Higher Secondary Public Schools in Delhi," IZA Discussion Papers 11670, Institute for the Study of Labor (IZA).
    17. Goldhaber, Dan & Walch, Joe, 2012. "Strategic pay reform: A student outcomes-based evaluation of Denver's ProComp teacher pay initiative," Economics of Education Review, Elsevier, vol. 31(6), pages 1067-1083.
    18. Harris, Douglas N. & Sass, Tim R., 2014. "Skills, productivity and the evaluation of teacher performance," Economics of Education Review, Elsevier, vol. 40(C), pages 183-204.

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