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The Stata command felsdvreg to fit a linear model with two high-dimensional fixed effects

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  • Thomas Cornelissen

    (University of Hannover)

Abstract

This article proposes a memory-saving decomposition of the design matrix to facilitate the estimation of a linear model with two high-dimensional fixed effects. A common way to fit such a model is to take into account one of the effects by including dummy variables and to sweep out the other effect by the within transformation (fixed-effects transformation). If the number of panel units is high, creating and storing the dummy variables can involve prohibitively large computer-memory requirements. The memory-saving procedure to set up the moment matrices for estimation presented in this article can reduce the memory requirements considerably. The companion Stata ado-file felsdvreg implements the estimation method, takes care of identification issues, and provides useful summary statistics. Copyright 2008 by StataCorp LP.

Suggested Citation

  • 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.
  • Handle: RePEc:tsj:stataj:v:8:y:2008:i:2:p:170-189
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    References listed on IDEAS

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    3. David Margolis, 1995. "High Wage Workers and High Wage Firms," Post-Print halshs-00378229, HAL.
    4. John M. Abowd & Francis Kramarz & David Margolis, 1999. "High Wage Workers and High Wage Firms," Post-Print halshs-00353892, HAL.
    5. 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.
    6. 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.
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