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Influence functions for linear regression (with an application to regression adjustment)

Author

Listed:
  • Ben Jann
Abstract
Influence functions are useful, for example, because they provide an easy and flexible way to estimate standard errors. This paper contains a brief overview of influence functions in the context of linear regression and illustrates how their empirical counterparts can be computed in Stata, both for unweighted data and for weighted data. Influence functions for regression-adjustment estimators of average treatment effects are also covered.

Suggested Citation

  • Ben Jann, 2019. "Influence functions for linear regression (with an application to regression adjustment)," University of Bern Social Sciences Working Papers 32, University of Bern, Department of Social Sciences, revised 30 Mar 2019.
    • Handle: RePEc:bss:wpaper:32
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    File URL: https://boris.unibe.ch/130362/1/jann-2019-influencefunctions.pdf
    File Function: Revised version
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    Citations

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    Cited by:

    1. Arceo-Gomez, Eva O. & Campos-Vazquez, Raymundo M. & Esquivel, Gerardo & Alcaraz, Eduardo & Martinez, Luis A. & Lopez, Norma G., 2023. "The impact of COVID-19 infection on labor outcomes of Mexican formal workers," World Development Perspectives, Elsevier, vol. 29(C).
    2. Judit Krekó & Balázs Munkácsy & Márton Csillag & Ágota Scharle, 2022. "A job trial subsidy for youth:cheap labour or a screening device?," CERS-IE WORKING PAPERS 2222, Institute of Economics, Centre for Economic and Regional Studies.
    3. Ben Jann, 2020. "Influence functions continued. A framework for estimating standard errors in reweighting, matching, and regression adjustment," University of Bern Social Sciences Working Papers 35, University of Bern, Department of Social Sciences, revised 31 Aug 2020.

    More about this item

    Keywords

    influence function; sampling variance; sampling weights; standard error; linear regression; mean difference; regression adjustment; average treatment effect; causal inference;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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