Bo Hu

I work on functional data analysis in both cross-sectional and time-series settings, contributing to the literature through theoretical and empirical research. My work develops methods for both stationary and nonstationary functional data and extends traditional econometric tools to infinite-dimensional objects, including trajectories of income, family structure, and social influence; stock-return distributions; and yield curves. These methods enable richer modeling of dynamic and structural relationships in economic data.

I am also interested in human development from both micro and macro perspectives. I study intergenerational mobility and inequality, focusing on how family, environment, and policy shape economic opportunities across generations. My projects examine cross-sectional heterogeneity, such as how mobility patterns vary by race, parental education, and other characteristics, as well as temporal heterogeneity, such as how the timing of parental income, family structure, and neighborhood conditions over the course of childhood influences long-term outcomes. These studies combine nonparametric, functional-data, Markov-chain, and machine-learning methods to uncover the complex mechanisms underlying persistent inequality.

My work also includes a global study documenting how national development banks complement commercial banks in providing long-term finance, and an analysis of state-owned enterprise reforms in transitional economies that reveals how policy burdens and distorted incentives shape reform outcomes.

My work has appeared in leading journals such as Journal of Applied Econometrics, Sociological Methods & Research, and World Development.