job market paper

Comparing Variance Estimators: a Test-Based Relative-Efficiency Approach (link)

Abstract: When constructing Wald tests, consistency is the key property required for the variance estimator. This property ensures asymptotic validity of Wald tests and confidence intervals. Classical efficiency comparisons of hypothesis tests indicate all consistent variance estimators lead to equivalent Wald tests. This paper develops a simple relative efficiency measure which leads to several new conclusions. These include quantifying the power loss associated with using cluster-robust variance estimators when using overly coarse clusters, recommending particular kernels for estimating the asymptotic variance in quantile regression, and comparing the power of Anderson-Rubin tests to the standard Wald test. As a byproduct, the asymptotic distributions of several test statistics are derived under fixed alternatives. Simulation evidence indicates the new asymptotic efficiency measure provides good finite-sample predictions. In an application using data from the American Community Survey, it is demonstrated how to use the new approach for conducting power analysis when looking at the effect of minimum wage increases on employment.

published Papers

Staying at Home: Mobility Effects of COVID-19, with John Stromme and Anson Zhou.

• Covid Economics: Vetted and Real-Time Papers, Issue 4, 86-102, April 2020

• Vox EU briefing here.

• Longer version.

Offline training for improving online performance of a genetic algorithm based optimization model for hourly multi-reservoir operation, with Duan Chen, Arturo S. Leon, Claudio Fuentes, and Qiuwen Chen.

• Environmental modelling & software, Volume 96, 46-57, October 2017

Working Papers

• Comparing Variance Estimators: a Test-Based Relative-Efficiency Approach (JMP)

• Characterizing the Power of the t-test for Heavy Tailed Data

Preliminary Work, in Progress

• Heteroskedastic-robust variance estimators for heavy-tailed data

• Robust Tests in Weak-Instrumental Variables Models

• Improved testing in partially linear models with many regressors

Teaching

University of Wisconsin-Madison:

• Spring 2021: MS Econometrics II (ECON 705) with Prof. Jack Porter and Prof. Harold Chiang.

• Fall 2020: PhD Math Camp/Math for Economists (ECON 703) with Prof. Anna Bykhovskaya.

• Fall 2019: PhD Econometrics I (ECON 709) with Prof. Bruce Hansen and Prof. Jack Porter.

• Fall 2017: Economic Approach to Current Issues (ECON 100) with Prof. Christopher Sullivan.

• Spring 2017: Principles of Macroeconomics (ECON 102) with Prof. Elizabeth Kelly.

• Fall 2016: Principles of Macroeconomics (ECON 102) with Prof. Steven Rick.

Oregon State University:

• Fall 2015: Introduction to Statistical Methods (ST 351)

• Fall 2014, Winter 2015, Spring 2015: Principles of Statistics (ST 201)