A nonparametric Bayesian methodology for regression discontinuity designs

Multiple imputation regression discontinuity designs: Alternative to regression discontinuity designs to estimate the local average treatment effect at the cutoff

“…Monte Carlo simulations are conducted under 112 different settings, each repeated 5,000 times, and the results of the simulations show that MIRDDs perform well in terms of bias, root mean squared error, coverage, and interval length in comparison with the standard RDD method. Also, additional simulations exhibit promising results compared to the state-of-theart RDD methods by Calonico, Cattaneo, and Titiunik (2014) and Branson et al (2019).…”

“…Simulation runs are repeated 5,000 times for each data generation process just as in Imbens and Kalyanaraman (2012) and Calonico, Cattaneo, and Titiunik (2014). Designs 1 to 4 were first used in the simulations of Imbens and Kalyanaraman (2012), and subsequently used in Calonico, Cattaneo, and Titiunik (2014) and Branson et al (2019). Designs 5 and 6 were first used in the simulations of Calonico, Cattaneo, and Titiunik (2014), and subsequently used in Branson et al (2019).…”

“…Designs 1 to 4 were first used in the simulations of Imbens and Kalyanaraman (2012), and subsequently used in Calonico, Cattaneo, and Titiunik (2014) and Branson et al (2019). Designs 5 and 6 were first used in the simulations of Calonico, Cattaneo, and Titiunik (2014), and subsequently used in Branson et al (2019). Design 7 was first used in Branson et al (2019).…”

“…Designs 5 and 6 were first used in the simulations of Calonico, Cattaneo, and Titiunik (2014), and subsequently used in Branson et al (2019). Design 7 was first used in Branson et al (2019).…”

“…While the sample size was set to 500 in Imbens and Kalyanaraman (2012), Calonico, Cattaneo, and Titiunik (2014), and Branson et al (2019), the sample size in the current study is set to 6,558. This larger sample size is chosen for the following reasons.…”