Regression Discontinuity Applications with Rounding Errors in the Running Variable

By Yingying Dong. 2015

103 citations recorded by the source corpus.

Subfields: Demography, General Health Professions, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4874fa74db8f23a538f219ffba67e4e35633b498

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Demography

1. Brigham R. Frandsen
2. Andrea Mercatanti
3. Yingying Dong

In General Health Professions

1. Brigham R. Frandsen
2. Fan Li
3. Andrea Mercatanti
4. Yingying Dong

In Statistics and Probability

1. Brigham R. Frandsen
2. Fan Li
3. Andrea Silvestrini
4. Andrea Mercatanti
5. Yingying Dong
6. T. Makinen
7. M. D. Cattaneo
8. R. Titiunik

Citations in the corpus

• Testing for Manipulation in the Regression Discontinuity Design When the Running Variable Is Discrete (2016, background): “Dong (2012) develops a biascorrected estimation procedure to account for rounding error when the discrete running variable is a rounded version of an underlying continuous variable.”
• Regression Discontinuity Design with Continuous Measurement Error in the Running Variable (2017, unknown): “Dong (2014), Barreca, Guldi, Lindo & Waddell (2011) and Barreca, Lindo & Waddell (2011) consider rounding errors.4 Battistin et al. (2009) consider contaminated or corrupted data sampling, in which the observed running variable Z 4Lee & Card (2008) also study the consequences of discrete running…”
• A regression discontinuity design for ordinal running variables: Evaluating central bank purchases of corporate bonds (2019, background): “Dong, Y. (2015). Regression Discontinuity Applications with Rounding Errors in the Running Variable.”
• Regression Discontinuity Designs (2021, unknown): “Dong, Lee, and Gou (2021) consider an RD design with a continuous (rather than binary or multi-valued) treatment, within the continuity framework, and show that RD causal effects can be identified based on changes in the probability distribution of the continuous treatment at the cutoff. Caetano, Ca”
• Inference in Regression Discontinuity Designs with a Discrete Running Variable (2016, unknown): “See Dong (2015) for an analysis of RDDs with this type of measurement error.”
• Jump or Kink? Identification of Binary Treatment Regression Discontinuity Design without the Discontinuity (2014, background): “The standard RD model requires only continuity for identification but not differentiability of these functions (See discussion in Hahn, Todd and van der Klaauw 2001 and Dong 2014).”