Arbeitspapier

Testing continuity of a density via g-order statistics in the regression discontinuity design

In the regression discontinuity design (RDD), it is common practice to assess the credibility of the design by testing the continuity of the density of the running variable at the cut-off, e.g., McCrary (2008). In this paper we propose a new test for continuity of a density at a point based on the so-called g-order statistics, and study its properties under a novel asymptotic framework. 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 effective observations local to the cut-off is often small. Thus, while traditional asymptotics in RDD require a growing number of observations local to the cut-off as n Ç É, our framework allows for the number q of observations local to the cut-off to be fixed as n Ç É. The new test is easy to implement, asymptotically valid under weaker conditions than those used by competing methods, exhibits finite sample validity under stronger conditions than those needed for its asymptotic validity, and has favorable power properties against certain alternatives. In a simulation study, we find that the new test controls size remarkably well across designs. We finally apply our test to the design in Lee (2008), a well-known application of the RDD to study incumbency advantage.

Sprache
Englisch

Erschienen in
Series: cemmap working paper ; No. CWP20/18

Klassifikation
Wirtschaft
Hypothesis Testing: General
Semiparametric and Nonparametric Methods: General
Thema
Regression discontinuity design
g-ordered statistics
sign tests
continuity
density

Ereignis
Geistige Schöpfung
(wer)
Bugni, Federico A.
Canay, Ivan A.
Ereignis
Veröffentlichung
(wer)
Centre for Microdata Methods and Practice (cemmap)
(wo)
London
(wann)
2018

DOI
doi:10.1920/wp.cem.2018.2018
Handle
Letzte Aktualisierung
10.03.2025, 11:41 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Bugni, Federico A.
  • Canay, Ivan A.
  • Centre for Microdata Methods and Practice (cemmap)

Entstanden

  • 2018

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