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Award Abstract #1357561

New Methodological Developments for Inference in the Regression-Discontinuity Design

NSF Org: SES
Divn Of Social and Economic Sciences
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Initial Amendment Date: April 11, 2014
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Latest Amendment Date: April 11, 2014
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Award Number: 1357561
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Award Instrument: Standard Grant
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Program Manager: Nancy A. Lutz
SES Divn Of Social and Economic Sciences
SBE Direct For Social, Behav & Economic Scie
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Start Date: July 1, 2014
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End Date: June 30, 2017 (Estimated)
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Awarded Amount to Date: $276,756.00
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Investigator(s): Matias Cattaneo cattaneo@umich.edu (Principal Investigator)
Rocio Titiunik (Co-Principal Investigator)
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Sponsor: University of Michigan Ann Arbor
3003 South State St. Room 1062
Ann Arbor, MI 48109-1274 (734)763-6438
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NSF Program(s): METHOD, MEASURE & STATS,
ECONOMICS
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Program Reference Code(s):
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Program Element Code(s): 1333, 1320

ABSTRACT

This award funds the development of new methods for the statistical analysis of social science data. The project focuses on 'regression discontinuity' (RD) models, which are very widely used in practice. This kind of statistical model is used to estimate the effect of a treatment on an outcome when unobserved factors may threaten the validity of the statistical analysis. While RD models are widely used to analyze data, many important methodological and statistical features of the models are not well understood. This project develops novel methods for the analysis of RD models and applies the results to concrete empirical problems in Economics and other social sciences.

The research develops new methodological and practical tools for estimation, inference and falsification of RD designs. This includes a method that accounts for the fact that RD estimators are sensitive to the choice of bandwidth, a method that is valid in finite samples, the analysis of RDs with multiple cutoffs and the development of new tools for spatial RD designs.

Broader impacts of this research will come from the use of these new methods to provide better insight for economic policy.


PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Luke Keele, Rocio Titiunik. "Geographic Boundaries as Regression Discontinuities," Political Analysis, v.23, 2015, p. 127.

Matias D. Cattaneo, Brigham Frandsen, Rocio Titiunik. "Randomization Inference in the Regression Discontinuity Design: An Application to Party Advantages in the U.S. Senate," Journal of Causal Inference, v.3, 2015, p. 1.

Rocio Titiunik. "Can Big Data Solve the Fundamental Problem of Causal Inference?," PS: Political Science & Politics, v.48, 2015, p. 74.

Sebastian Calonico, Matias D. Cattaneo, Rocio Titiunik. "Robust Data-Driven Inference in the Regression-Discontinuity Design," Stata Journal, v.14, 2014, p. 909.

Sebastian Calonico, Matias D. Cattaneo, Rocio Titiunik. "Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs," Econometrica, v.82, 2014, p. 2295.

Luke Keele, Rocio Titiunik. "Natural Experiments Based on Geography," Political Science Research and Methods, v.4, 2016, p. 65.

Robert Erikson, Rocio Titiunik. "Using Regression Discontinuity to Uncover the Personal Incumbency Advantage," Quarterly Journal of Political Science, v.10, 2015, p. 101.

Sebastian Calonico, Matias D. Cattaneo, Rocio Titiunik. "Optimal Data-Driven Regression Discontinuity Plots," Journal of the American Statistical Association, v.110, 2015, p. 1753.

Sebastian Calonico, Matias D. Cattaneo, Rocio Titiunik. "rdrobust: An R Package for Robust Nonparametric Inference in Regression-Discontinuity Designs," R Journal, v.7, 2015, p. 38.

 

Please report errors in award information by writing to: awardsearch@nsf.gov.

 

 

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