Statistics and Computing

, Volume 15, Issue 3, pp 197–215 | Cite as

Matching estimators and optimal bandwidth choice

Article

Abstract

Optimal bandwidth choice for matching estimators and their finite sample properties are examined. An approximation to their MSE is derived, as a basis for a plug-in bandwidth selector. In small samples, this approximation is not very accurate, though. Alternatively, conventional cross-validation bandwidth selection is considered and performs rather well in simulation studies: Compared to standard pair-matching, kernel and ridge matching achieve reductions in MSE of about 25 to 40%. Local linear matching and weighting perform poorly. Furthermore, the scope for developing better bandwidth selectors seems to be limited for ridge matching, but non-negligible for kernel and local linear matching.

Keywords

covariate adjustment nonparametric regression propensity score missing data counterfactual treatment effect 

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Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  1. 1.University College London and University of St. GallenSt. GallenSwitzerland
  2. 2.Institute for the Study of Labor (IZA)Bonn

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