Bias-Aware Inference in Fuzzy Regression Discontinuity Designs
SpeakerChristoph Rothe (University of Mannheim)
LocationUvA - E-building, Roetersstraat 11, Room E5.22 Amsterdam
Date and time
November 15, 2019
16:00 - 17:15
Fuzzy regression discontinuity (FRD) designs are used frequently in many areas of applied economics. We argue that the conﬁdence intervals based on nonparametric local linear regression that are commonly reported in empirical FRD studies can have poor ﬁnite sample coverage properties for reasons related to their general construction based on the delta method, and to how they account for smoothing bias. We therefore propose new conﬁdence sets, which are based on an Anderson-Rubin-type construction. These conﬁdence sets are bias-aware, in the sense that they explicitly take into account the exact smoothing bias of the local linear estimators on which they are based. They are simple to compute, highly eﬃcient, and have excellent coverage properties in ﬁnite samples. They are also valid under weak identiﬁcation(that is, if the jump in treatment probabilities at the threshold is small) and irrespective of whether the distribution of the running variable is continuous, discrete, or of some intermediate form.
Joint with Claudia Noack.