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20172025
most citedA Practical Introduction to Regression Discontinuity Designs: Foundations

537 citations · 667 across the 23 of their papers we have counts for

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Showing 2018Show all

8 papers · 1 filter

econ.EM2018

Simple Local Polynomial Density Estimators

Matias D. Cattaneo, Michael Jansson, Xinwei Ma

This paper introduces an intuitive and easy-to-implement nonparametric density estimator based on local polynomial techniques. The estimator is fully boundary adaptive and automati…

econ.EM2018

Regression Discontinuity Designs Using Covariates

Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell +1

We study regression discontinuity designs when covariates are included in the estimation. We examine local polynomial estimators that include discrete or continuous covariates in a…

econ.EM2018

Characteristic-Sorted Portfolios: Estimation and Inference

Matias D. Cattaneo, Richard K. Crump, Max H. Farrell +1

Portfolio sorting is ubiquitous in the empirical finance literature, where it has been widely used to identify pricing anomalies. Despite its popularity, little attention has been…

econ.EM2018

Optimal Bandwidth Choice for Robust Bias Corrected Inference in Regression Discontinuity Designs

Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell

Modern empirical work in Regression Discontinuity (RD) designs often employs local polynomial estimation and inference with a mean square error (MSE) optimal bandwidth choice. This…

econ.EM2018

Extrapolating Treatment Effects in Multi-Cutoff Regression Discontinuity Designs

Matias D. Cattaneo, Luke Keele, Rocio Titiunik +1

In non-experimental settings, the Regression Discontinuity (RD) design is one of the most credible identification strategies for program evaluation and causal inference. However, R…

econ.EM2018

Coverage Error Optimal Confidence Intervals for Local Polynomial Regression

Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell

This paper studies higher-order inference properties of nonparametric local polynomial regression methods under random sampling. We prove Edgeworth expansions for statistics an…