A Simple and Successul Method to Shrink the Weight
AbstractWe 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.
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Bibliographic InfoPaper provided by Department of Economics, University of Konstanz in its series Working Paper Series of the Department of Economics, University of Konstanz with number 2013-05.
Length: 41 pages
Date of creation: 23 Mar 2013
Date of revision:
Find related papers by 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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