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A simple feasible procedure to fit models with high-dimensional fixed effects

  • Paulo Guimarães

    ()

    (University of South Carolina)

  • Pedro Portugal

    ()

    (Banco de Portugal)

In this article, we describe an iterative approach for the estimation of linear regression models with high-dimensional fixed effects. This approach is computationally intensive but imposes minimum memory requirements. We also show that the approach can be extended to nonlinear models and to more than two high-dimensional fixed effects. Copyright 2010 by StataCorp LP.

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Article provided by StataCorp LP in its journal Stata Journal.

Volume (Year): 10 (2010)
Issue (Month): 4 (December)
Pages: 628-649

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Handle: RePEc:tsj:stataj:v:10:y:2010:i:4:p:628-649
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  1. William Greene, 2004. "The behaviour of the maximum likelihood estimator of limited dependent variable models in the presence of fixed effects," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 98-119, 06.
  2. 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.
  3. 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.
  4. Martyn Andrews & Thorsten Schank & Richard Upward, 2006. "Practical fixed-effects estimation methods for the three-way error-components model," Stata Journal, StataCorp LP, vol. 6(4), pages 461-481, December.
  5. Gary Chamberlain, 1980. "Analysis of Covariance with Qualitative Data," Review of Economic Studies, Oxford University Press, vol. 47(1), pages 225-238.
  6. Thomas Cornelissen, 2008. "The Stata command felsdvreg to fit a linear model with two high-dimensional fixed effects," Stata Journal, StataCorp LP, vol. 8(2), pages 170-189, June.
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