Abstract

Matching estimators are widely applied in practice for their great in

tuitive appeal.

However, simple matching estimators with a fixed number of

matches (M0) are generally inefficient. In this article, we propose matching estimators with a variable number of matches to gain efficiency via rematching.

Rather than increasing M0 to gain precision, which introduces an increase in

bias, the key is to rematch the treated units from the opposite direction to utilize unmatched control units. Our rematching estimators are applicable to both

the average treatment effect and its counterpart for the treated population. The

proposed rematching estimators are proven asymptotically valid and uniformly

more efficient than matching estimators with the same M0. Simulation results

confirm that the proposed rematching estimators substantially improve the simple matching estimators in finite samples. As an empirical illustration, we apply

the estimators proposed in this article to the National Supported Work data.

Information

Preprint No.SS-2024-0306
Manuscript IDSS-2024-0306
Complete AuthorsLam Lam Hui, Kin Wai Chan
Corresponding AuthorsKin Wai Chan
Emailskinwaichan@cuhk.edu.hk

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Acknowledgments

The authors would like to thank the anonymous referees, an Associate Editor, and the Editor for their constructive comments that improved the scope

and theoretical development of the paper. We are particularly grateful to

the referees for providing valuable suggestions to improve the argument in

the proof of Theorem 1.

Rematching Estimators

Supplementary Materials

The supplement contains technical assumptions, proofs of main results,

additional simulation results, a detailed description of the real-data application, and a summary of existing work; see Sections S1–S5, respectively.

Supplementary materials are available for download.