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 joint work with Federico Bugni, 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 grows, our framework allows for the number q of observations local to the cut-off to be fixed as n grows. 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. You can find a copy of the paper here.

We have also finished the first version of a Stata package that implements the new test we propose. You can download the package from the Bitbucket repository (Rdcont), which includes the ado file with an example of how to use it. Visit the software page here for additional Stata and R packages.