Advances in Regression Discontinuity Design
Sunday, Jan. 8, 2023 1:00 PM - 3:00 PM (CST)
- Chair: Peter Siminski, University of Technology Sydney
When Can We Ignore Measurement Error in the Running Variable?
AbstractIn many empirical applications of regression discontinuity designs, the running variable used by the administrator to assign treatment is only observed with error. We show that, provided the observed running variable (i) correctly classifies the treatment assignment, and (ii) affects the conditional means of the potential outcomes smoothly, ignoring the measurement error nonetheless yields an estimate with a causal interpretation: the average treatment effect for units with the value of the observed running variable equal to the cutoff. To accommodate various types of measurement error, we propose to conduct inference using recently developed bias-aware methods, which re- main valid even when discreteness or irregular support in the observed running variable may lead to partial identication. We illustrate the results for both sharp and fuzzy designs in an empirical application.
Visual Inference and Graphical Representation in Regression Discontinuity Designs
AbstractDespite the widespread use of graphs in empirical research, little is known about readers’ ability to process the statistical information they are meant to convey (“visual inference”). We study visual inference within the context of regression discontinuity (RD) designs by measuring how accurately readers identify discontinuities in graphs produced from data generating processes calibrated on 11 published papers from leading economics journals. First, we assess the effects of different graphical representation methods on visual inference using randomized experiments. We find that bin widths and fit lines have the largest impacts on whether participants correctly perceive the presence or absence of a discontinuity. Incorporating the experimental results into two decision theoretical criteria adapted from the recent economics literature, we find that using small bins with no fit lines to construct RD graphs performs well and recommend it as a starting point to practitioners. Second, we compare visual inference with widely used econometric inference procedures. We find that visual inference achieves similar or lower type I error rates and complements econometric inference.
Optimal Model Selection in RDD and Related Settings Using Placebo Zones
AbstractWe propose a new model-selection algorithm for Regression Discontinuity Design, Regression Kink Design, and related IV estimators. Candidate models are assessed within a `placebo zone' of the running variable, where the true effects are known to be zero. The approach yields an optimal combination of bandwidth, polynomial, and any other choice parameters. It can also inform choices between classes of models (e.g. RDD versus cohort-IV) and any other choices, such as covariates, kernel, or other weights. We outline sufficient conditions under which the approach is asymptotically optimal. The approach also performs favorably under more general conditions in a series of Monte Carlo simulations. We demonstrate the approach in an evaluation of changes to Minimum Supervised Driving Hours in the Australian state of New South Wales. We also re-evaluate evidence on the effects of Head Start and Minimum Legal Drinking Age. We conclude with practical advice for researchers.
- C1 - Econometric and Statistical Methods and Methodology: General
- C4 - Econometric and Statistical Methods: Special Topics