About

Welcome! I am an energy and environmental economist. My research uses applied microeconomics to study topics such as residential energy consumption and efficiency, recycling, air quality, and applied econometrics.

Research topics

• Engineering
• Econometrics
• Microeconomics
• Computer Science
• Economics
• Mechanical engineering
• Business
• Environmental economics
• Agricultural economics
• Psychology
• Environmental science
• Mathematics

Selected publications

• Energy price pass-through with long-term contracts

  Economics Letters · 2026-01-02

  article1st authorCorresponding
  PublisherDOI

• Energy Price Pass-Through With Long-Term Contracts

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Who Heeds the Call to Conserve in an Energy Emergency? Evidence from Smart Thermostat Data

  Journal of the Association of Environmental and Resource Economists · 2025-02-26 · 1 citations

  article1st authorCorresponding
  PublisherDOI

• Is There a Trade-Off between Forest Expansion and Agriculture?

  Land Economics · 2025-03-07 · 1 citations

  article
  PublisherDOI

• Tax Policy and Energy Markets: Evaluating the Efficacy of Production Tax Relief on Oil & Gas Activity

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Work from home, polarization, and new residential construction during COVID-19

  Applied Economics Letters · 2024-09-02 · 1 citations

  article1st authorCorresponding
  PublisherDOI

• The effect of extreme temperatures on evictions

  Journal of Environmental Economics and Management · 2024-09-20 · 1 citations

  article1st authorCorresponding
  PublisherDOI

• Addressing sample selection bias for machine learning methods

  Journal of Applied Econometrics · 2024-01-17 · 1 citations

  article1st authorCorresponding

  Summary We study approaches for adjusting machine learning methods when the training sample differs from the prediction sample on unobserved dimensions. The machine learning literature predominately assumes selection only on observed dimensions. Common approaches are to weight or include variables that influence selection as solutions to selection on observables. Simulation results show that selection on unobservables increases mean squared prediction error using popular machine‐learning algorithms. Common machine learning practices such as weighting or including variables that influence selection into the training or prediction sample often worsen sample selection bias. We propose two control function approaches that remove the effects of selection bias before training and find that they reduce mean‐squared prediction error in simulations. We apply these approaches to predicting the vote share of the incumbent in gubernatorial elections using previously observed re‐election bids. We find that ignoring selection on unobservables leads to substantially higher predicted vote shares for the incumbent than when the control function approach is used.

  PublisherDOI

• The Pandemic, Work from Home, and New Residential Construction

  SSRN Electronic Journal · 2024-01-01

  articleOpen access1st authorCorresponding
  PublisherDOI

• Is there a tradeoff between afforestation and agriculture? Evidence from India

  SSRN Electronic Journal · 2024-01-01

  articleOpen access
  PublisherDOI

Frequent coauthors

• Samantha Cameron

  University of California, Davis

  2 shared

• Graham Lewis

  University of Minnesota

  2 shared

• Vikrant Kamble

  2 shared

• Sarah Goldgar

  2 shared

• Matthew E. Oliver

  Georgia Institute of Technology

  2 shared

• Daniel Dench

  Georgia Institute of Technology

  1 shared

• Alyssa Carlson

  University of Missouri

  1 shared

• Jim Crozier

  Georgia Institute of Technology

  1 shared

Labs

• CLEAR Economics LabPI

Education

• PhD, Department of Economics

  Michigan State University

  2019

• MA, Department of Economics

  Michigan State University

  2016

• BA, Economics, Foreign Affairs

  University of Virginia

  2014