Periodic Reporting for period 2 - RD-ADVANCE (Advancing Econometric Methods for Analyzing Data from Regression Discontinuity Designs)
Período documentado: 2020-03-01 hasta 2021-08-31
Over the past two decades, regression discontinuity (RD) designs have become one of empirical economics’ most popular strategies for estimating causal effects from observational data. In such designs, units are assigned to the treatment group if and only if a special covariate, or running variable, falls above a known cutoff value. Under mild conditions, those units close to the cutoff are as good as randomly assigned to receive the treatment, which provides a simple and transparent source of identification of the treatment’s causal effect.
To goal of this project is to extend the range of tools available to applied researchers working with data from RD designs. This involves methodological research in econometrics and statistics, as well as some applied work with real data. The research will lead to more accurate and easy to implement methods for estimation and inference, and address practical issues that often challenge the validity of RD analysis.
Given the huge popularity of RD designs, and the projects’s focus on practical methods, it has the potential to have a sizable impact on empirical economic research in a number of policy relevant areas, such as education and public finance; but also on other branches of science where researchers commonly work with observational data, such as sociology or epidemiology.
To goal of this project is to extend the range of tools available to applied researchers working with data from RD designs. This involves methodological research in econometrics and statistics, as well as some applied work with real data. The research will lead to more accurate and easy to implement methods for estimation and inference, and address practical issues that often challenge the validity of RD analysis.
Given the huge popularity of RD designs, and the projects’s focus on practical methods, it has the potential to have a sizable impact on empirical economic research in a number of policy relevant areas, such as education and public finance; but also on other branches of science where researchers commonly work with observational data, such as sociology or epidemiology.
This project is divided into three parts. The first part develops methods for incorporating covariates and group structures into the analysis of RD designs by adapting modern machine learning method. The second part considers RD designs with a discrete running variable. It shows that current state-of-the-art inference procedures are likely to be misleading in such settings, and develops new confidence intervals for causal effects. The third part develops methods for estimation and inference that account for manipulation in RD designs. Here manipulation refers to any strategic action taken by the actors within the respective institutional context that leads to observational units on different sides of the cutoff being non-comparable. The part develops a general framework for manipulation with corresponding nonparametric methods for estimation and inference, and considers various extensions.
At this intermediate stage, the project has already lead to some influential publications on inference with a discrete running variable and manipulation in RD designs. Another working paper is currently under review, and several further technical reports are close to completion.