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The Variance of Non-Parametric Treatment Effect Estimators in the Presence of Clustering

Author

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  • Samuel G. Hanson

    (Harvard Business School)

  • Adi Sunderam

    (Harvard Business School)

Abstract

Nonparametric estimators of treatment effects are often applied in settings where clustering may be important. We provide a general methodology for consistently estimating the variance of a large class of nonparametric estimators, including the simple matching estimator, in the presence of clustering. Software for implementing our variance estimator is available in Stata. © 2012 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.

Suggested Citation

  • Samuel G. Hanson & Adi Sunderam, 2012. "The Variance of Non-Parametric Treatment Effect Estimators in the Presence of Clustering," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1197-1201, November.
  • Handle: RePEc:tpr:restat:v:94:y:2012:i:4:p:1197-1201
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    Keywords

    treatment effects; matching estimators; clustering;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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