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.
Bibliographical noteFunding 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 .
© 2017 Elsevier B.V.
- Minimum distance estimator
- Multiple running variables
- Regression discontinuity
ASJC Scopus subject areas
- Economics and Econometrics