TY - JOUR
T1 - Regression discontinuity
T2 - review with extensions
AU - Choi, Jin young
AU - Lee, Myoung jae
N1 - Funding Information:
Acknowledgements The authors are grateful to the Editor and two reviewers for their helpful comments. The research of Myoung-jae Lee has been supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2015S1A5A2A01009718).
Publisher Copyright:
© 2016, Springer-Verlag Berlin Heidelberg.
PY - 2017/12/1
Y1 - 2017/12/1
N2 - In treatment effect analysis, often the treatment takes a particular structure: ‘on’ if an underlying continuous variable crosses a threshold, and ‘off’ otherwise. Such a treatment occurs in various institutional settings such as a test score crossing a threshold to graduate, or income falling below a threshold to qualify for an aid. In this kind of cases, the study design is called ‘regression discontinuity (RD)’, which is popular in analyzing observational data, as long as the treatment takes the required form. This paper reviews RD to convey its essentials, and provides some extensions. First, the main RD idea based on local randomization due to an institutional/legal break is introduced. Second, treatment effects identified by RD are explored. Third, popular RD estimators are reviewed. Fourth, main specification tests are examined. Fifth, special RD topics are reviewed. Also, an empirical illustration is provided.
AB - In treatment effect analysis, often the treatment takes a particular structure: ‘on’ if an underlying continuous variable crosses a threshold, and ‘off’ otherwise. Such a treatment occurs in various institutional settings such as a test score crossing a threshold to graduate, or income falling below a threshold to qualify for an aid. In this kind of cases, the study design is called ‘regression discontinuity (RD)’, which is popular in analyzing observational data, as long as the treatment takes the required form. This paper reviews RD to convey its essentials, and provides some extensions. First, the main RD idea based on local randomization due to an institutional/legal break is introduced. Second, treatment effects identified by RD are explored. Third, popular RD estimators are reviewed. Fourth, main specification tests are examined. Fifth, special RD topics are reviewed. Also, an empirical illustration is provided.
KW - Instrumental variable estimator
KW - Nonparametrics
KW - Regression discontinuity
UR - http://www.scopus.com/inward/record.url?scp=84957582718&partnerID=8YFLogxK
U2 - 10.1007/s00362-016-0745-z
DO - 10.1007/s00362-016-0745-z
M3 - Article
AN - SCOPUS:84957582718
SN - 0932-5026
VL - 58
SP - 1217
EP - 1246
JO - Statistical Papers
JF - Statistical Papers
IS - 4
ER -