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

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.

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

SSRN Electronic Journal ◽

10.2139/ssrn.3495778 ◽

2019 ◽

Author(s):

Yang He ◽

Otávio Bartalotti

Keyword(s):

Confidence Intervals ◽

Regression Discontinuity ◽

Fuzzy Regression ◽

Wild Bootstrap ◽

Regression Discontinuity Designs

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Inference in Regression Discontinuity Designs with a Discrete Running Variable

The American Economic Review ◽

10.1257/aer.20160945 ◽

2018 ◽

Vol 108 (8) ◽

pp. 2277-2304 ◽

Cited By ~ 43

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)

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Class-Size Caps, Sorting, and the Regression-Discontinuity Design

The American Economic Review ◽

10.1257/aer.99.1.179 ◽

2009 ◽

Vol 99 (1) ◽

pp. 179-215 ◽

Cited By ~ 90

Author(s):

Miguel Urquiola ◽

Eric Verhoogen

Keyword(s):

Class Size ◽

Student Outcomes ◽

Regression Discontinuity ◽

Regression Discontinuity Design ◽

Household Characteristics ◽

Research Designs ◽

Education Market ◽

The Relationship

This paper examines how schools' choices of class size and households' choices of schools affect regression-discontinuity-based estimates of the effect of class size on student outcomes. We build a model in which schools are subject to a class-size cap and an integer constraint on the number of classrooms, and higher-income households sort into higher-quality schools. The key prediction, borne out in data from Chile's liberalized education market, is that schools at the class-size cap adjust prices (or enrollments) to avoid adding an additional classroom, which generates discontinuities in the relationship between enrollment and household characteristics, violating the assumptions underlying regression-discontinuity research designs. (JEL D12, I21, I28, O15)

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