Minimum distance estimator for sharp regression discontinuity with multiple running variables

Jin young Choi, Myoung jae Lee

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


In typical regression discontinuity, a running variable (or ‘score’) crosses a cutoff to determine a treatment. There are, however, many regression discontinuity cases where multiple scores have to cross all of their cutoffs to get treated. One approach to deal with these cases is one-dimensional localization using a single score on the subpopulation with all the other scores already crossing the cutoffs (“conditional one-dimensional localization approach, CON”), which is, however, inconsistent when partial effects are present which occur when some, but not all, scores cross their cutoffs. Another approach is multi-dimensional localization explicitly allowing for partial effects, which is, however, less efficient than CON due to more localizations than in CON. We propose a minimum distance estimator that is at least as efficient as CON, yet consistent even when partial effects are present. A simulation study demonstrates these characteristics of the minimum distance estimator.

Original languageEnglish
Pages (from-to)10-14
Number of pages5
JournalEconomics Letters
Publication statusPublished - 2018 Jan

Bibliographical note

Funding Information:
The authors are grateful to a reviewer who provided detailed constructive comments. Myoung-jae Lee’s research has been supported by a Korea University Grant .

Publisher Copyright:
© 2017 Elsevier B.V.


  • Minimum distance estimator
  • Multiple running variables
  • Regression discontinuity

ASJC Scopus subject areas

  • Finance
  • Economics and Econometrics


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