  We 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. Our Stata commands implement the procedure and compare its
performance to other approaches.
