Welcome! My name is Samuel Engle, and I am a Ph.D. candidate in economics at the University of Wisconsin-Madison.
I will be joining the
faculty of the University of Exeter Business School as a Lecturer (Assistant Professor) in the Department of Economics this fall.

Primary Research Field: Econometrics

Curriculum Vitae (C.V.): pdf

Email: sengle2 [at] wisc.edu

References: Jack Porter (committee chair), Bruce Hansen, Mikkel Sølvsten, Harold Chiang.

Office: # 7310 William H. Sewell Social Science Building, 1180 Observatory Dr, Madison, WI 53706

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.

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.

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


University of Wisconsin-Madison:

Oregon State University:

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

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