Research

Research Interests: Applied Econometric and Statistical Methodologies (B41, C40), Simulation-Based Inference (C15), Model Evaluation, Validation, and Selection (C52), Forecasting and Time Series Methods (C53), Machine Learning Applications, Meta-Regression Methodology

Notes:

*The codes above are JEL codes for economists

*My five-year research agenda includes 6 projects not listed here, details are in my job market package and available upon request

Highlights

I presented my current job market paper in both Camp Econometrics XIX and the 13th meeting of International Association of Applied Econometrics (IAAE)
I have
- 2 manuscripts under review 4 working papers
- 1 paper is pre-print available a 5 year research agenda, including 6 articulated research ideas

Publications:

- Daigneault, A. & Gendron, J. (2025) Where the Trees Fall: Macroeconomic Forecasts for Forest-Reliant States (Paper Link, Github Link)
  - - Summary: Builds a macroeconomic forecasting model to forecast macroeconomic variables for the forest sector at the industry/state level (including key states) by using a Vector Error Correction (VEC) Model
    - Contributions:
      - We provide a framework for policy analysis by integrating our previous identification of industry-dependent states within the forestry sector
      - We provide a framework for forecasting in other natural-resource reliant industries (such as mining, agriculture, and energy production)

Figure 1. Simulation Design for the job market paper under review

Figure 2. Modeling workflow for the forestry paper under review

Working Papers
Gendron, J., Habibnia, A., & Jaimes Sandoval A. Automated Diagnostics for Meta-Regressions: A Machine Learning Framework
- - Summary: Uses machine learning for meta-regression model selection and verifies model selection with robustness checks
Gendron, J. & Habibnia, A. Efficacy of Location-level Fixed Effects in Experimental Economics: A Simulation Study on Heterogeneity in Location
- - Summary: This chapter of my dissertation uses simulations to test which meta-regression method performs optimally when there is heterogeneity in only the location each study was conducted in
Dickinson, S. & Gendron, J. Cost Saving with Misspecification Testing - Assuring Optimal Experimental Design
- - Summary: Constructs a framework for experimental economists to assess design robustness pre-experiment
Gendron, J. From Normality to Reality: Improving Experimental Economic Analysis with Distributions
- - Summary: Analyzes distribution details of experimental economic behavioral data, like trust and reciprocity (i.e. trustworthiness)

Works in Progress
Gendron, J. & Habibnia, A. Permutation Tests Improve Upon Model-Free Dependence Measures
- - Summary: This uses simulations to showcase the poor performance of standard model-free dependence measures compared to more advanced permutation testing and moving-block bootstrapping

Invited Talks:

September 19, 2025