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New Evidence on the Finite Sample Properties of Propensity Score Matching and Reweighting Estimators

  • Busso, Matias

    ()

    (Inter-American Development Bank)

  • DiNardo, John

    ()

    (University of Michigan)

  • McCrary, Justin

    ()

    (University of California, Berkeley)

Currently available asymptotic results in the literature suggest that matching estimators have higher variance than reweighting estimators. The extant literature comparing the finite sample properties of matching to specific reweighting estimators, however, has concluded that reweighting performs far worse than even the simplest matching estimator. We resolve this puzzle. We show that the findings from the finite sample analyses are not inconsistent with asymptotic analysis, but are very specific to particular choices regarding the implementation of reweighting, and fail to generalize to settings likely to be encountered in actual empirical practice. In the DGPs studied here, reweighting typically outperforms propensity score matching.

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Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 3998.

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Length: 30 pages
Date of creation: Feb 2009
Date of revision:
Handle: RePEc:iza:izadps:dp3998
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