scholarly journals Local Polynomial Order in Regression Discontinuity Designs

2020 ◽  
Author(s):  
Zhuan Pei ◽  
David Lee ◽  
David Card ◽  
Andrea Weber
Keyword(s):  
Regression Discontinuity ◽  
Local Polynomial ◽  
Regression Discontinuity Designs

scholarly journals Regression Discontinuity Designs Using Covariates

2019 ◽  
Vol 101 (3) ◽  
pp. 442-451 ◽  
Author(s):  
Sebastian Calonico ◽  
Matias D. Cattaneo ◽  
Max H. Farrell ◽  
Rocío Titiunik
Keyword(s):  
Mean Squared Error ◽  
Regression Discontinuity ◽  
Standard Errors ◽  
Local Polynomial ◽  
Squared Error ◽  
Software Packages ◽  
Continuous Covariates ◽  
Regression Functions ◽  
Regression Discontinuity Designs ◽  
Robust Standard Errors

We study regression discontinuity designs when covariates are included in the estimation. We examine local polynomial estimators that include discrete or continuous covariates in an additive separable way, but without imposing any parametric restrictions on the underlying population regression functions. We recommend a covariate-adjustment approach that retains consistency under intuitive conditions and characterize the potential for estimation and inference improvements. We also present new covariate-adjusted mean-squared error expansions and robust bias-corrected inference procedures, with heteroskedasticity-consistent and cluster-robust standard errors. We provide an empirical illustration and an extensive simulation study. All methods are implemented in R and Stata software packages.

Power calculations for regression-discontinuity designs

2019 ◽  
Vol 19 (1) ◽  
pp. 210-245 ◽  
Author(s):  
Matias D. Cattaneo ◽  
Rocío Titiunik ◽  
Gonzalo Vazquez-Bare
Keyword(s):  
Sample Size ◽  
Regression Discontinuity ◽  
Sample Selection ◽  
R Package ◽  
Power Calculations ◽  
Local Polynomial ◽  
Regression Discontinuity Designs ◽  
Local Polynomial Estimation ◽  
Polynomial Estimation ◽  
Inference Methods

In this article, we introduce two commands, rdpow and rdsampsi, that conduct power calculations and survey sample selection when using local polynomial estimation and inference methods in regression-discontinuity designs. rdpow conducts power calculations using modern robust bias-corrected local polynomial inference procedures and allows for new hypothetical sample sizes and bandwidth selections, among other features. rdsampsi uses power calculations to compute the minimum sample size required to achieve a desired level of power, given estimated or user-supplied bandwidths, biases, and variances. Together, these commands are useful when devising new experiments or surveys in regression-discontinuity designs, which will later be analyzed using modern local polynomial techniques for estimation, inference, and falsification. Because our commands use the communitycontributed (and R) package rdrobust for the underlying bandwidths, biases, and variances estimation, all the options currently available in rdrobust can also be used for power calculations and sample-size selection, including preintervention covariate adjustment, clustered sampling, and many bandwidth selectors. Finally, we also provide companion R functions with the same syntax and capabilities.

scholarly journals Equivalence Testing for Regression Discontinuity Designs

2020 ◽  
pp. 1-17
Author(s):  
Erin Hartman
Keyword(s):  
Political Science ◽  
Current Practice ◽  
Regression Discontinuity ◽  
Regression Function ◽  
Superior Performance ◽  
Equivalence Testing ◽  
Simulation Studies ◽  
Equivalence Tests ◽  
Regression Discontinuity Designs ◽  
Testing Approach

Abstract Regression discontinuity (RD) designs are increasingly common in political science. They have many advantages, including a known and observable treatment assignment mechanism. The literature has emphasized the need for “falsification tests” and ways to assess the validity of the design. When implementing RD designs, researchers typically rely on two falsification tests, based on empirically testable implications of the identifying assumptions, to argue the design is credible. These tests, one for continuity in the regression function for a pretreatment covariate, and one for continuity in the density of the forcing variable, use a null of no difference in the parameter of interest at the discontinuity. Common practice can, incorrectly, conflate a failure to reject evidence of a flawed design with evidence that the design is credible. The well-known equivalence testing approach addresses these problems, but how to implement equivalence tests in the RD framework is not straightforward. This paper develops two equivalence tests tailored for RD designs that allow researchers to provide statistical evidence that the design is credible. Simulation studies show the superior performance of equivalence-based tests over tests-of-difference, as used in current practice. The tests are applied to the close elections RD data presented in Eggers et al. (2015b).

scholarly journals The Choice of Neighborhood in Regression Discontinuity Designs

2017 ◽  
Vol 3 (2) ◽  
pp. 134-146
Author(s):  
Matias D. Cattaneo ◽  
Gonzalo Vazquez-Bare
Keyword(s):  
Regression Discontinuity ◽  
Regression Discontinuity Designs

scholarly journals Beyond Manipulation: Administrative Sorting in Regression Discontinuity Designs

2020 ◽  
Vol 8 (1) ◽  
pp. 164-181
Author(s):  
Cristian Crespo
Keyword(s):  
Regression Discontinuity ◽  
Internal Validity ◽  
Treatment Assignment ◽  
Research Designs ◽  
Regression Discontinuity Designs ◽  
Administrative Procedures

Abstract This paper elaborates on administrative sorting, a threat to internal validity that has been overlooked in the regression discontinuity (RD) literature. Variation in treatment assignment near the threshold may still not be as good as random even when individuals are unable to precisely manipulate the running variable. This can be the case when administrative procedures, beyond individuals’ control and knowledge, affect their position near the threshold non-randomly. If administrative sorting is not recognized it can be mistaken as manipulation, preventing fixing the running variable and leading to discarding viable RD research designs.

scholarly journals Regression discontinuity designs: A guide to practice

2008 ◽  
Vol 142 (2) ◽  
pp. 615-635 ◽  
Author(s):  
Guido W. Imbens ◽  
Thomas Lemieux
Keyword(s):  
Regression Discontinuity ◽  
Regression Discontinuity Designs
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