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A Simple and Successul Method to Shrink the Weight

Author

Listed:
  • Winfried Pohlmeier

    () (Department of Economics, University of Konstanz, Germany)

  • Ruben R. Seiberlich

    () (Department of Economics, University of Konstanz, Germany)

  • S. Derya Uysal

    () (Economics and Finance Department, Institute for Advanced Studies Vienna, Autriche)

Abstract

We propose a simple way to improve the efficiency of the average treatment effect on propensity score based estimators. As the weights become arbitrarily large for the propensity scores being close to one or zero, we propose to shrink the propensity scores away from these boundaries. Using a comprehensive Monte Carlo study we show that this simple method substantially reduces the mean squared error of the estimators in finite samples.

Suggested Citation

  • Winfried Pohlmeier & Ruben R. Seiberlich & S. Derya Uysal, 2013. "A Simple and Successul Method to Shrink the Weight," Working Paper Series of the Department of Economics, University of Konstanz 2013-05, Department of Economics, University of Konstanz.
  • Handle: RePEc:knz:dpteco:1305
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    File URL: http://www.uni-konstanz.de/FuF/wiwi/workingpaperseries/WP_05-Pohlmeier-Seiberlich-Uysal_2013.pdf
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    Cited by:

    1. repec:eee:csdana:v:115:y:2017:i:c:p:91-102 is not listed on IDEAS
    2. Frölich, Markus & Huber, Martin & Wiesenfarth, Manuel, 2017. "The finite sample performance of semi- and non-parametric estimators for treatment effects and policy evaluation," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 91-102.

    More about this item

    Keywords

    Econometric evaluation; propensity score; penalizing; shrinkage; average treatment effect;

    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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