Jonathan Gendron
Applied econometrician helping researchers choose the right methods to get the right inferences!
Applied econometrician helping researchers choose the right methods to get the right inferences!
Linkedin: https://www.linkedin.com/in/jonathangendron
Github: https://github.com/jegendron
Email: jegendron@vt.edu
Google Scholar: Link
ORCID: Link
Currently Researching:
Automated Diagnostics for Meta-Regressions: A Machine Learning Framework
Efficacy of Location-level Fixed Effects in Experimental Economics: A Simulation Study on Heterogeneity in Location
Permutation Tests Improve Upon Model-Free Dependence Measures
Cost Saving with Misspecification Testing - Assuring Optimal Experimental Design
From Normality to Reality: Improving Experimental Economic Analysis with Distributions
Job Market Paper:
Contributions: The following are new to the literature
Providing a practitioner's guide to model selection
Testing performance of relevant methods with various degrees of location and time heterogeneity
Testing the efficacy of two new methods in the experimental economics and meta-analysis simulation literature
Abstract: In this paper, we conduct a simulation study with subject-level data to evaluate conventional meta-regression approaches (study-level random, fixed, and mixed effects) against seven methodology specifications new to meta-regressions that control joint heterogeneity in location and time (including a new one that we introduce). We systematically vary heterogeneity levels to assess statistical power, estimator bias and model robustness for each methodology specification. This assessment focuses on three aspects: performance under joint heterogeneity in location and time, the effectiveness of our proposed settings incorporating location fixed effects and study-level fixed effects with a time trend, as well as guidelines for model selection. The results show that jointly modeling heterogeneity when heterogeneity is in both dimensions improves performance compared to modeling only one type of heterogeneity.
Academic Biography:
I am currently a 5th year Ph.D. candidate on the 2025-2026 academic job market. My research lies at the intersection of applied econometrics, statistical methodology, and computational modeling, with a particular focus on simulation-based validation and meta-regression techniques. I aim to bridge methodological rigor with practical, policy-relevant insights, applying advanced econometric tools to areas ranging from macroeconomic forecasting to experimental design and machine learning.
In addition to research, for the past three years I have had extensive experience in
teaching seven sections across four undergraduate courses in economics with
three sections in intermediate courses, including applied econometrics
teaching evaluations consistently above the college and department's average)
academic work
in addition to the standard graduate assistantship
professional service
refereeing journal articles
providing letters of recommendation
speaking at alumni events for my alma matter
etc (further details available in my CV).
Literature Any Economist Should Read:
Carvalho & Guerra (2025) Is Crime Displacement Inevitable? Evidence from Police Crackdowns in Fortaleza, Brazil, https://arxiv.org/abs/2503.13571
Boug et al. (2024) Getting Back on Track: Forecasting After Extreme Observations, (link)
Spanos (2023) Revisiting the Large n (Sample Size) Problem: How to Avert Spurious Significance Results, https://doi.org/10.3390/stats6040081
Spanos (2021) Statistical modeling and inference in the era of Data Science and Graphical Causal modeling, https://doi.org/10.1111/joes.12483
Badenes-Ribera et al (2016) Misconceptions of the p-value among Chilean and Italian Academic Psychologists, https://doi.org/10.3389/fpsyg.2016.01247
Spanos (2010) Akaike-type criteria and the reliability of inference: Model selection versus statistical model specification, https://doi.org/10.1016/j.jeconom.2010.01.011
Spanos & McGuirk (2001) The Model Specification Problem from a Probabilistic Reduction Perspective, https://www.jstor.org/stable/1244803
Zuckerman et al (1993) Contemporary Issues in the Analysis of Data: A Survey of 551 Psychologists, https://www.jstor.org/stable/40062503
Literature Any Grad Student Should Read:
Bellemare (2022) Doing Economics: What You Should Have Learned in Grad School―But Didn’t, (link)
Bolker (1998) Writing Your Thesis in Fifteen Minutes a Day, (link)