338 citations · 769 across the 16 of their papers we have counts for
6 papers · 1 filter
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…
Deep Neural Networks for Estimation and Inference
Max H. Farrell, Tengyuan Liang, Sanjog Misra
We study deep neural networks and their use in semiparametric inference. We establish novel rates of convergence for deep feedforward neural nets. Our new rates are sufficiently fa…
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…
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…
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…
Large Sample Properties of Partitioning-Based Series Estimators
Matias D. Cattaneo, Max H. Farrell, Yingjie Feng
We present large sample results for partitioning-based least squares nonparametric regression, a popular method for approximating conditional expectation functions in statistics, e…