# Robust Nonparametric Confidence Intervals for Regression‐Discontinuity Designs

@article{Calonico2014RobustNC, title={Robust Nonparametric Confidence Intervals for Regression‐Discontinuity Designs}, author={Sebastian Calonico and M. D. Cattaneo and R. Titiunik}, journal={Econometrica}, year={2014}, volume={82}, pages={2295-2326} }

In the regression‐discontinuity (RD) design, units are assigned to treatment based on whether their value of an observed covariate exceeds a known cutoff. In this design, local polynomial estimators are now routinely employed to construct confidence intervals for treatment effects. The performance of these confidence intervals in applications, however, may be seriously hampered by their sensitivity to the specific bandwidth employed. Available bandwidth selectors typically yield a “large… Expand

#### 1,641 Citations

Bootstrap Confidence Intervals for Sharp Regression Discontinuity Designs with the Uniform Kernel

- Mathematics
- 2016

This paper develops a novel bootstrap procedure to obtain robust bias-corrected confidence intervals in regression discontinuity (RD) designs using the uniform kernel. The procedure uses a residual… Expand

Optimal Bandwidth Choice for Robust Bias Corrected Inference in Regression Discontinuity Designs

- Economics, Mathematics
- 2018

Modern empirical work in Regression Discontinuity (RD) designs often employs local polynomial estimation and inference with a mean square error (MSE) optimal bandwidth choice. This bandwidth yields… Expand

Bootstrap Confidence Intervals for Sharp Regression Discontinuity Designs

- Mathematics
- 2017

Abstract
This chapter develops a novel bootstrap procedure to obtain robust bias-corrected confidence intervals in regression discontinuity (RD) designs. The procedure uses a wild bootstrap from a… Expand

Robust Data-Driven Inference in the Regression-Discontinuity Design

- Mathematics
- 2014

In this article, we introduce three commands to conduct robust data-driven statistical inference in regression-discontinuity (RD) designs. First, we present rdrobust, a command that implements the… Expand

A Unified Robust Bootstrap Method for Sharp/Fuzzy Mean/Quantile Regression Discontinuity/Kink Designs

- Mathematics
- 2017

Computation of asymptotic distributions is known to be a nontrivial and delicate task for the regression discontinuity designs (RDD) and the regression kink designs (RKD). It is even more complicated… Expand

Regression Discontinuity and Heteroskedasticity Robust Standard Errors: Evidence from a Fixed-Bandwidth Approximation

- Mathematics
- 2018

Abstract In regression discontinuity designs (RD), for a given bandwidth, researchers can estimate standard errors based on different variance formulas obtained under different asymptotic frameworks.… Expand

Coverage Optimal Empirical Likelihood Inference for Regression Discontinuity Design

- Computer Science, Economics
- 2020

This paper investigates both firstorder and second-order asymptotic properties and derives the coverage optimal bandwidth which minimizes the leading term in the coverage error expansion and finds that Bartlett corrected empirical likelihood inference further improves the coverage accuracy. Expand

On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference

- Mathematics, Economics
- 2015

ABSTRACT Nonparametric methods play a central role in modern empirical work. While they provide inference procedures that are more robust to parametric misspecification bias, they may be quite… Expand

Optimized Regression Discontinuity Designs

- Mathematics, Computer Science
- Review of Economics and Statistics
- 2019

This work proposes an alternative method for estimation and statistical inference in regression discontinuity designs that uses numerical convex optimization to directly obtain the finite-sample-minimax linear estimator for the regression discontinuit parameter, subject to bounds on the second derivative of the conditional response function. Expand

rdrobust: An R Package for Robust Nonparametric Inference in Regression-Discontinuity Designs

- Mathematics, Computer Science
- R J.
- 2015

An introduction to the R package rdrobust, which offers an array of data-driven local-polynomial and partitioning-based inference procedures for RD designs, and introduces three main functions implementing several data- driven nonparametric point and con- fidence intervals estimators, bandwidth selectors, and plotting procedures useful for RD empirical applications. Expand

#### References

SHOWING 1-10 OF 106 REFERENCES

Robust Data-Driven Inference in the Regression-Discontinuity Design

- Mathematics
- 2014

In this article, we introduce three commands to conduct robust data-driven statistical inference in regression-discontinuity (RD) designs. First, we present rdrobust, a command that implements the… Expand

On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference

- Mathematics, Economics
- 2015

ABSTRACT Nonparametric methods play a central role in modern empirical work. While they provide inference procedures that are more robust to parametric misspecification bias, they may be quite… Expand

rdrobust: An R Package for Robust Nonparametric Inference in Regression-Discontinuity Designs

- Mathematics, Computer Science
- R J.
- 2015

An introduction to the R package rdrobust, which offers an array of data-driven local-polynomial and partitioning-based inference procedures for RD designs, and introduces three main functions implementing several data- driven nonparametric point and con- fidence intervals estimators, bandwidth selectors, and plotting procedures useful for RD empirical applications. Expand

On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Estimation ∗

- 2013

This paper studies the effect of bias correction on confidence interval estimators in the context of kernel-based nonparametric density estimation. We consider explicit plug-in bias correction but,… Expand

Empirical Likelihood for Regression Discontinuity Design

- Mathematics
- 2011

This paper proposes empirical likelihood based inference methods for causal effects identified from regression discontinuity designs. We consider both the sharp and fuzzy regression discontinuity… Expand

Empirical likelihood for regression discontinuity design

- Mathematics
- 2015

This paper proposes empirical likelihood based inference methods for causal effects identified from regression discontinuity designs. We consider both the sharp and fuzzy regression discontinuity… Expand

Randomization Inference in the Regression Discontinuity Design: An Application to Party Advantages in the U.S. Senate

- Mathematics
- 2014

Abstract In the Regression Discontinuity (RD) design, units are assigned a treatment based on whether their value of an observed covariate is above or below a fixed cutoff. Under the assumption that… Expand

Randomization Inference in the Regression Discontinuity Design : An Application to the Study of Party Advantages in the U . S . Senate ∗

- 2013

In the Regression Discontinuity (RD) design, units are assigned a treatment based on whether their value of an observed covariate (the “score”) is above or below a fixed cutoff. Under the assumption… Expand

Weak Identification in Fuzzy Regression Discontinuity Designs

- Mathematics
- 2016

In fuzzy regression discontinuity (FRD) designs, the treatment effect is identified through a discontinuity in the conditional probability of treatment assignment. We show that when identification is… Expand

Optimal Data-Driven Regression Discontinuity Plots

- Mathematics
- 2015

Exploratory data analysis plays a central role in applied statistics and econometrics. In the popular regression-discontinuity (RD) design, the use of graphical analysis has been strongly advocated… Expand