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


  • Ben Jann



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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    More about this item


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

    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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