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Dr. Herdelin’s professional background spans both academic research and public service, having previously served as an Economist with the United States Department of the Treasury and held faculty appointments at Villanova University, Ursinus College, and Stockton University. He holds a Ph.D. in Economics from the New School for Social Research, an M.S. in Applied Economics from Johns Hopkins University, an M.S. in Financial Economics from West Texas A&M University, and a B.A. from Thomas Edison State University. He also holds professional certifications as a Certified Business Economist (CBE) and a Certified Forecaster.

Dr. Herdelin’s research integrates macroeconomic dynamics, stability theory, quantitative policy modeling, computational economics, and economic education pedagogy, grounding each of these subfields within a Post Keynesian framework. His work specializes in evaluating the structural stability, distributional changes, and evolutionary trajectories of macroeconomic systems from a heterodox perspective. Within quantitative policy modeling, his research utilizes vector autoregression (VAR) frameworks to evaluate how central bank mandates, Taylor rules, and labor market slack interact over multi-decade horizons to affect macro-distribution.

A secondary focus of his current research involves advancing economic education and interdisciplinary pedagogy by bridging Post Keynesian economic frameworks with formal mathematical modeling. He constructs continuous-time systems formalized through differential equations, leveraging Jacobian matrices and qualitative phase diagrams to evaluate systemic stability boundaries. To assess higher-dimensional structural dynamics, his research applies the mathematical mechanics of the Routh-Hurwitz criterion.

Dedicated to experiential learning, Dr. Herdelin trains students to actively use and interact with diverse computational and econometric tools, including MATLAB and Stata. Through this hands-on approach, he empowers students to bridge theoretical frameworks with quantitative application, ensuring they can effectively manipulate analytical models and interpret real-world data within a practical macroeconomic and financial context.