Joshua Shea

GitHub

Curriculum Vitae

jkcshea [at] illinois [dot] edu


Assistant Professor of Economics at the University of Illinois Urbana Champaign.

Research interests: Applied econometrics, Econometrics, and Discrimination.

Working Papers

ivmte: An R Package for Implementing Marginal Treatment Effect Methods (Accepted at Observational Studies; with Alexander Torgovitsky)

Instrumental variable (IV) strategies are widely used to estimate causal effects in economics, political science, epidemiology, psychology, and other fields. When there is unobserved heterogeneity in causal effects, standard linear IV estimators only represent effects for complier subpopulations (Imbens and Angrist, 1994). Marginal treatment effect (MTE) methods (Heckman and Vytlacil, 1999, 2005) allow researchers to use additional assumptions to extrapolate beyond complier subpopulations. We discuss a flexible framework for MTE methods based on linear regression and the generalized method of moments. We show how to implement the framework using the ivmte package for R.

Paper R package (ivmte)

Testing for Racial Bias in Police Traffic Searches (Awarded Best Paper Prize at EWMES 2022)

I develop a framework to detect and measure racial bias in police traffic searches amid sample selection and statistical discrimination. By modeling the search decisions stochastically, I allow the direction and intensity of bias to depend on the officer’s belief of how likely a driver carries contraband. Sharp bounds on various measures of intensity are derived. This framework also enables me to evaluate each officer separately, thereby allowing for unrestricted heterogeneity in officer search preferences and beliefs.

New draft coming soon

Published Papers

Inference for Support Vector Regression with l1 Regularization (AEA Papers and Proceedings, 2021; with Yuehao Bai, Hung Ho, Guillaume A. Pouliot)

We provide large sample distribution theory for support vector regression (SVR) with l1-norm, along with error bars for the SVR regression coefficients. Although a classical Wald confidence interval obtains from our theory, its implementation inherently depends on the choice of a tuning parameter which scales the variance estimate and thus the width of the error bars. We address this shortcoming by further proposing an alternative large sample inference method based on the inversion of a novel test statistic which displays competitive power properties and does not depend on the choice of a tuning parameter.

Paper R package (l1svr)

Works in Progress

How Local are Local Average Treatment Effects? (with Christine Blandhol, John Bonney, Magne Mogstad, Alexander Torgovitsky)