7 papers
lspartition: Partitioning-Based Least Squares Regression
Matias D. Cattaneo, Max H. Farrell, Yingjie Feng
Nonparametric partitioning-based least squares regression is an important tool in empirical work. Common examples include regressions based on splines, wavelets, and piecewise poly…
nprobust: Nonparametric Kernel-Based Estimation and Robust Bias-Corrected Inference
Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell
Nonparametric kernel density and local polynomial regression estimators are very popular in Statistics, Economics, and many other disciplines. They are routinely employed in applie…
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…