Bohdan is a PhD student in Economics at the University of California, Los Angeles, specializing in theoretical econometrics. He previously earned a Master of Arts in Economics from Indiana University (2023) and a Master of Arts in Economics with honors from the Kyiv School of Economics, jointly with the University of Houston (2020).
His research focuses on the intersection of econometrics and machine learning, with an emphasis on inference under weak identification in high-dimensional settings. His current work studies pre-testing procedures for weak identification when first-stage relationships are estimated using machine learning methods.
He is also interested in structural econometrics, particularly demand estimation in differentiated product markets. More broadly, He is interested in nonparametric approaches to identification and estimation in this setting.