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

By M. D. Cattaneo, Brigham R. Frandsen, R. Titiunik. 2015

148 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/51dd57727d4654e74ad36d207928684097a87147

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In Statistics and Probability

Citations in the corpus

Approximate permutation tests and induced order statistics in the regression discontinuity design (2015, background): “The asymptotic results are what primarily separates this article from others in the RDD literature that have advocated for the use of permutation tests (see, e.g. Cattaneo et al., 2015; Sales and Hansen, 2015; Ganong and Jäger, 2015).”
A Permutation Test for the Regression Kink Design (2018, methodology): “Wealso fi nd broadly similar results using a di ff erent permutation test proposed by Cattaneo, Frandsen and Titiunik (2015, CFT in the following), which holds the location of the discontinuity fi xed and randomly varies which observations are assigned to the treatment and control.”
Econometric Methods for Program Evaluation (2018, methodology): “Cattaneo et al. (2015) introduce a Fisherian model for inference in RD designs using a local randomization assumption.”
The Incumbency Curse: Weak Parties, Term Limits, and Unfulfilled Accountability (2017, methodology): “10 Throughout this article, we adopt a continuity-based approach to analyze and interpret the RD design—but see Cattaneo, Frandsen, and Titiunik (2015) for a randomization-based alternative.”
A Practical Introduction to Regression Discontinuity Designs (2019, methodology): “We provide complete replication codes in both R and Stata for the entire empirical analysis discussed throughout the Element and, in addition, we provide replication codes for a second empirical illustration using the data of Cattaneo, Frandsen, and Titiunik (2015).”
Rdrobust: Software for Regression-discontinuity Designs (2017, background): ““under-smooth” (that is, choose a larger number of bins) manually either by scaling up the scale() with the nbins”
Interpreting Regression Discontinuity Designs with Multiple Cutoffs (2016, methodology): “…single-cutoff RD analysis is to employ either local polynomial methods (Calonico, Cattaneo, and Farrell 2016; Calonico, Cattaneo, and Titiunik 2014a, 2014b, 2015b; Hahn et al. 2001) or local randomization methods (Cattaneo et al. 2015; Cattaneo, Titiunik, and Vazquez-Bare 2016a, 2016b; Lee 2008).”
Regression Discontinuity Designs Using Covariates (2018, methodology): “(formally or informally) thought of as a randomized experiment near the cutoff (Lee, 2008; Cattaneo et al., 2015), and the inclusion of covariates is often used in the analysis of experiments to increase precision, inclusion of covariates in RD estimation seems natural.”
External Validity in Fuzzy Regression Discontinuity Designs (2019, unknown): “See Van Der Klaauw (2008), Imbens and Lemieux (2008), and Lee and Lemieux (2010) for recent surveys, and Cook (2008) for a historical perspective.”