Daniel McLeod’s academic background is empirical industrial organization and applied econometrics. He has experience working in the areas of antitrust and competition, economic cost measurement in the airline and railroad industries, and productivity measurement in the postal and electric utility industries. Additionally, in the energy practice, he has been involved in the calibration of price and revenue caps, helped design and evaluate incentive regulation plans, performed and critiqued cost benchmarking studies, and estimated the load impacts of EV smart charging algorithms and critical peak pricing demand response programs. As a graduate student, Daniel taught classes in econometrics and machine learning. His research proposed a novel econometric approach to estimating marginal costs in the airline industry and quantified the impacts of airline mergers using both structural models of the industry and emerging deep learning algorithms.
