New Evidence on the Finite Sample Properties of Propensity Score Matching and Reweighting Estimators
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.
|Date of creation:||Feb 2009|
|Date of revision:|
|Contact details of provider:|| Postal: IZA, P.O. Box 7240, D-53072 Bonn, Germany|
Phone: +49 228 3894 223
Fax: +49 228 3894 180
Web page: http://www.iza.org
|Order Information:|| Postal: IZA, Margard Ody, P.O. Box 7240, D-53072 Bonn, Germany|
When requesting a correction, please mention this item's handle: RePEc:iza:izadps:dp3998. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Mark Fallak)
If references are entirely missing, you can add them using this form.