2023/07/26 by Francesco Ruggieri, Ruggieri, Francesco
Mathematics · #Advanced Causal Inference Techniques #Econometrics (econ.EM) #FOS: Economics and business #Statistical Methods and Bayesian Inference #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.2307.14203
openalex publication_date 2023/07/26 · openalex created_date 2023/07/28 · openalex updated_date 2026/07/28
I propose a novel argument to identify economically interpretable intertemporal treatment effects in dynamic regression discontinuity designs (RDDs). Specifically, I develop a dynamic potential outcomes model and reformulate two assumptions from the difference-in-differences literature, no anticipation and common trends, to attain point identification of cutoff-specific impulse responses. The estimand of each target parameter can be expressed as the sum of two static RDD contrasts, thereby allowing for nonparametric estimation and inference with standard local polynomial methods. I also propose a nonparametric approach to aggregate treatment effects across calendar time and treatment paths, leveraging a limited path independence restriction to reduce the dimensionality of the parameter space. I apply this method to estimate the dynamic effects of school district expenditure authorizations on housing prices in Wisconsin.