scholarly journals Wild Bootstrap for Fuzzy Regression Discontinuity Designs: Obtaining Robust Bias-Corrected Confidence Intervals

2019 ◽  
Author(s):  
Yang He ◽  
Otávio Bartalotti
Keyword(s):  
Confidence Intervals ◽  
Regression Discontinuity ◽  
Fuzzy Regression ◽  
Wild Bootstrap ◽  
Regression Discontinuity Designs

scholarly journals Wild bootstrap for fuzzy regression discontinuity designs: obtaining robust bias-corrected confidence intervals

2020 ◽  
Vol 23 (2) ◽  
pp. 211-231
Author(s):  
Yang He ◽  
Otávio Bartalotti
Keyword(s):  
Confidence Intervals ◽  
Class Size ◽  
Student Outcomes ◽  
Regression Discontinuity ◽  
Fuzzy Regression ◽  
New Method ◽  
Wild Bootstrap ◽  
Bootstrap Procedure ◽  
Related Literature ◽  
Regression Discontinuity Designs

Summary This paper develops a novel wild bootstrap procedure to construct robust bias-corrected valid confidence intervals for fuzzy regression discontinuity designs, providing an intuitive complement to existing robust bias-corrected methods. The confidence intervals generated by this procedure are valid under conditions similar to the procedures proposed by Calonico et al. (2014) and related literature. Simulations provide evidence that this new method is at least as accurate as the plug-in analytical corrections when applied to a variety of data-generating processes featuring endogeneity and clustering. Finally, we demonstrate its empirical relevance by revisiting Angrist and Lavy (1999) analysis of class size on student outcomes.

scholarly journals Inference in Regression Discontinuity Designs with a Discrete Running Variable

2018 ◽  
Vol 108 (8) ◽  
pp. 2277-2304 ◽  
Author(s):  
Michal Kolesár ◽  
Christoph Rothe
Keyword(s):  
Confidence Intervals ◽  
Conditional Expectation ◽  
Regression Discontinuity ◽  
Model Misspecification ◽  
Standard Errors ◽  
Moderate Number ◽  
Regression Discontinuity Designs ◽  
The Common ◽  
Present Simulation ◽  
Theoretical Results

We consider inference in regression discontinuity designs when the running variable only takes a moderate number of distinct values. In particular, we study the common practice of using confidence intervals (CIs) based on standard errors that are clustered by the running variable as a means to make inference robust to model misspecification (Lee and Card 2008). We derive theoretical results and present simulation and empirical evidence showing that these CIs do not guard against model misspecification, and that they have poor coverage properties. We therefore recommend against using these CIs in practice. We instead propose two alternative CIs with guaranteed coverage properties under easily interpretable restrictions on the conditional expectation function. (JEL C13, C51, J13, J31, J64, J65)

scholarly journals Weak Identification in Fuzzy Regression Discontinuity Designs

2016 ◽  
Vol 34 (2) ◽  
pp. 185-196 ◽  
Author(s):  
Donna Feir ◽  
Thomas Lemieux ◽  
Vadim Marmer
Keyword(s):  
Regression Discontinuity ◽  
Fuzzy Regression ◽  
Weak Identification ◽  
Regression Discontinuity Designs

scholarly journals External Validity in Fuzzy Regression Discontinuity Designs

2019 ◽  
Vol 38 (3) ◽  
pp. 593-612 ◽  
Author(s):  
Marinho Bertanha ◽  
Guido W. Imbens
Keyword(s):  
External Validity ◽  
Regression Discontinuity ◽  
Fuzzy Regression ◽  
Regression Discontinuity Designs

scholarly journals Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs

Econometrica ◽  
2014 ◽  
Vol 82 (6) ◽  
pp. 2295-2326 ◽  
Author(s):  
Sebastian Calonico ◽  
Matias D. Cattaneo ◽  
Rocio Titiunik
Keyword(s):  
Confidence Intervals ◽  
Regression Discontinuity ◽  
Regression Discontinuity Designs ◽  
Nonparametric Confidence Intervals

scholarly journals Weak Identification in Fuzzy Regression Discontinuity Designs

2010 ◽  
Author(s):  
Vadim Marmer ◽  
Donna Feir ◽  
Thomas Lemieux
Keyword(s):  
Regression Discontinuity ◽  
Fuzzy Regression ◽  
Weak Identification ◽  
Regression Discontinuity Designs

The Effect of Serving “Breakfast After-the-Bell” Meals on School Absenteeism: Comparing Results From Regression Discontinuity Designs

2021 ◽  
pp. 016237372199157
Author(s):  
J. Jacob Kirksey ◽  
Michael A. Gottfried
Keyword(s):  
High Schools ◽  
Regression Discontinuity ◽  
Suburban Schools ◽  
Fuzzy Regression ◽  
School Absenteeism ◽  
National Data ◽  
Chronic Absenteeism ◽  
School Programs ◽  
Regression Discontinuity Designs ◽  
School Breakfast

With the rise in the availability of absenteeism data, it is clear that students are missing a staggering amount of school. Policymakers have focused efforts on identifying school programs that might reduce absenteeism. This study examined whether implementing the program “Breakfast After-the-Bell” (BAB) might reduce school absenteeism. Exploring longitudinal statewide datasets (Colorado and Nevada) containing school breakfast information linked to national data on chronic absenteeism rates, we used sharp and fuzzy regression discontinuity designs to examine the effects of BAB. Our findings suggest that schools serving BAB experienced declines in chronic absenteeism. The strongest effects were experienced by high schools, schools with higher rates of breakfast participation, schools serving universally free meals, and suburban schools. Implications are discussed.

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