Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs
2014; Wiley; Volume: 82; Issue: 6 Linguagem: Inglês
10.3982/ecta11757
ISSN1468-0262
AutoresSebastián Calónico, Matias D. Cattaneo, Rocío Titiunik,
Tópico(s)Statistical Methods and Bayesian Inference
ResumoEconometricaVolume 82, Issue 6 p. 2295-2326 Notes and Comments Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs Sebastian Calonico, Sebastian Calonico scalonico@bus.miami.edu Dept. of Economics, University of Miami, 5250 University Dr., Coral Gables, FL, 33124 U.S.A.Search for more papers by this authorMatias D. Cattaneo, Matias D. Cattaneo cattaneo@umich.edu Dept. of Economics, University of Michigan, 611 Tappan Ave., Ann Arbor, MI, 48109 U.S.A.Search for more papers by this authorRocio Titiunik, Rocio Titiunik titiunik@umich.edu Dept. of Political Science, University of Michigan, 505 S. State St., Ann Arbor, MI, 48109 U.S.A. This paper is a revised and extended version of our paper previously entitled “Robust Nonparametric Bias-Corrected Inference in the Regression Discontinuity Design” (first draft: November 2011). We are specially thankful to the co-editor for encouraging us to write this new version as well as for giving us very useful comments and suggestions on the previous drafts. In addition, for thoughtful comments and suggestions we thank the three anonymous referees, Alberto Abadie, Josh Angrist, Ivan Canay, Victor Chernozhukov, Max Farrell, Patrik Guggenberger, Hide Ichimura, Guido Imbens, Michael Jansson, Justin McCrary, Anna Mikusheva, Whitney Newey, Zhuan Pei, Andres Santos, Jeff Smith, Jeff Wooldridge, as well as seminar and conference participants at many institutions. Finally, we also thank Manuela Angelucci, Luis Alejos Marroquin, Habiba Djebbari, Paul Gertler, Dan Gilligan, Jens Ludwig, and Doug Miller for sharing and helping us with different data sets used in this research project. The authors gratefully acknowledge financial support from the National Science Foundation (SES 1357561). Search for more papers by this author Sebastian Calonico, Sebastian Calonico scalonico@bus.miami.edu Dept. of Economics, University of Miami, 5250 University Dr., Coral Gables, FL, 33124 U.S.A.Search for more papers by this authorMatias D. Cattaneo, Matias D. Cattaneo cattaneo@umich.edu Dept. of Economics, University of Michigan, 611 Tappan Ave., Ann Arbor, MI, 48109 U.S.A.Search for more papers by this authorRocio Titiunik, Rocio Titiunik titiunik@umich.edu Dept. of Political Science, University of Michigan, 505 S. State St., Ann Arbor, MI, 48109 U.S.A. This paper is a revised and extended version of our paper previously entitled “Robust Nonparametric Bias-Corrected Inference in the Regression Discontinuity Design” (first draft: November 2011). We are specially thankful to the co-editor for encouraging us to write this new version as well as for giving us very useful comments and suggestions on the previous drafts. In addition, for thoughtful comments and suggestions we thank the three anonymous referees, Alberto Abadie, Josh Angrist, Ivan Canay, Victor Chernozhukov, Max Farrell, Patrik Guggenberger, Hide Ichimura, Guido Imbens, Michael Jansson, Justin McCrary, Anna Mikusheva, Whitney Newey, Zhuan Pei, Andres Santos, Jeff Smith, Jeff Wooldridge, as well as seminar and conference participants at many institutions. Finally, we also thank Manuela Angelucci, Luis Alejos Marroquin, Habiba Djebbari, Paul Gertler, Dan Gilligan, Jens Ludwig, and Doug Miller for sharing and helping us with different data sets used in this research project. The authors gratefully acknowledge financial support from the National Science Foundation (SES 1357561). Search for more papers by this author First published: 23 December 2014 https://doi.org/10.3982/ECTA11757Citations: 1,121 AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract In the regression-discontinuity (RD) design, units are assigned to treatment based on whether their value of an observed covariate exceeds a known cutoff. In this design, local polynomial estimators are now routinely employed to construct confidence intervals for treatment effects. The performance of these confidence intervals in applications, however, may be seriously hampered by their sensitivity to the specific bandwidth employed. Available bandwidth selectors typically yield a “large” bandwidth, leading to data-driven confidence intervals that may be biased, with empirical coverage well below their nominal target. We propose new theory-based, more robust confidence interval estimators for average treatment effects at the cutoff in sharp RD, sharp kink RD, fuzzy RD, and fuzzy kink RD designs. Our proposed confidence intervals are constructed using a bias-corrected RD estimator together with a novel standard error estimator. For practical implementation, we discuss mean squared error optimal bandwidths, which are by construction not valid for conventional confidence intervals but are valid with our robust approach, and consistent standard error estimators based on our new variance formulas. In a special case of practical interest, our procedure amounts to running a quadratic instead of a linear local regression. More generally, our results give a formal justification to simple inference procedures based on increasing the order of the local polynomial estimator employed. We find in a simulation study that our confidence intervals exhibit close-to-correct empirical coverage and good empirical interval length on average, remarkably improving upon the alternatives available in the literature. All results are readily available in R and STATA using our companion software packages described in Calonico, Cattaneo, and Titiunik (2014d, 2014b). Citing Literature Volume82, Issue6November 2014Pages 2295-2326 RelatedInformation
Referência(s)