Approximate permutation tests and induced order statistics in the regression discontinuity design
Ivan Canay and
Vishal Kamat
Additional contact information
Vishal Kamat: Institute for Fiscal Studies and Toulouse School of Economics
No CWP33/16, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies
Abstract:
In the regression discontinuity design, it is common practice to asses the credibility of the design by testing whether the means of baseline covariates do not change at the cuto ff (or threshold) of the running variable. This practice is partly motivated by the stronger implication derived by Lee (2008), who showed that under certain conditions the distribution of baseline covariates in the RDD must be continuous at the cuto ff. We propose a permutation test based on the so-called induced ordered statistics for the null hypothesis of continuity of the distribution of baseline covariates at the cutoff ; and introduce a novel asymptotic framework to analyze its properties. The asymptotic framework is intended to approximate a small sample phenomenon: even though the total number n of observations may be large, the number of eff ective observations local to the cuto ff is often small. Thus, while traditional asymptotics in RDD require a growing number of observations local to the cuto ff as n ? 8 , our framework keeps the number q of observations local to the cutoff fixed as n ? 8. The new test is easy to implement, asymptotically valid under weak conditions, exhibits finite sample validity under stronger conditions than those needed for its asymptotic validity, and has favorable power properties relative to tests based on means. In a simulation study, we fi nd that the new test controls size remarkably well across designs. We then use our test to evaluate the validity of the design in Lee (2008), a well-known application of the RDD to study incumbency advantage.
Keywords: Regression discontinuity design; permutation tests; randomization tests; induced ordered statistics; rank tests. (search for similar items in EconPapers)
JEL-codes: C12 C14 (search for similar items in EconPapers)
Date: 2016-08-19
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
https://www.ifs.org.uk/uploads/cemmap/wps/cwp331616.pdf (application/pdf)
Our link check indicates that this URL is bad, the error code is: 404 Not Found (https://www.ifs.org.uk/uploads/cemmap/wps/cwp331616.pdf [302 Found]--> https://ifs.org.uk/uploads/cemmap/wps/cwp331616.pdf)
Related works:
Journal Article: Approximate Permutation Tests and Induced Order Statistics in the Regression Discontinuity Design (2018)
Working Paper: Approximate permutation tests and induced order statistics in the regression discontinuity design (2017)
Working Paper: Approximate permutation tests and induced order statistics in the regression discontinuity design (2017)
Working Paper: Approximate permutation tests and induced order statistics in the regression discontinuity design (2016)
Working Paper: Approximate permutation tests and induced order statistics in the regression discontinuity design (2015)
Working Paper: Approximate permutation tests and induced order statistics in the regression discontinuity design (2015)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:ifs:cemmap:33/16
Ordering information: This working paper can be ordered from
The Institute for Fiscal Studies 7 Ridgmount Street LONDON WC1E 7AE
mailbox@ifs.org.uk
Access Statistics for this paper
More papers in CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies The Institute for Fiscal Studies 7 Ridgmount Street LONDON WC1E 7AE. Contact information at EDIRC.
Bibliographic data for series maintained by Emma Hyman (emma_h@ifs.org.uk).