Assistant Professor of Economics
- PhD, University of California - Riverside, Economics
- MA, University of Southern California, Economics
- BA, University of California - Irvine, Quantitative Economics
Justin Dang is an assistant professor of economics at the University of San Diego's Knauss School of Business. He holds a PhD in Economics from the University of California - Riverside, a Master of Arts in Economics from the University of Southern California, and a Bachelor of Arts in Quantitative Economics from the University of California - Irvine. His research focuses on econometrics including nonparametric and semiparametric modeling and machine learning. Justin’s current research concentrates on deriving theoretical properties of machine learning-based estimators and estimating marginal effects and derivatives using machine learning with economic applications.
Selected Publications
- Dang, J., Maeder, N., Vamosiu, A. (2025) From Giving to Doing: Factors Influencing Alumni Volunteerism, The Journal of Business Leadership.
- Dang, J., Ullah, A. (2023) Generalized Kernel Regularized Least Squares Estimator with Parametric Error Covariance, Empirical Economics, 64, 3059–3088.
- Dang, J., Ullah, A. (2022) Machine-Learning-Based Semiparametric Time Series Conditional Variance: Estimation and Forecasting, Journal of Risk and Financial Management, 15(1), https://doi.org/10.3390/jrfm15010038.