Home

Research expertise: time series econometrics, macroeconometrics, panel data, epidemiology, machine learning

Research: I am interested in both econometric theory and applied econometrics. My main current focus is on developing statistically efficient methods for estimating dynamic causal effects via local projections with and without external instruments, and on developing computationally efficient methods for estimating high dimensional nonlinear models. 

I am additionally pursuing interdisciplinary work on bagging and random forests - at the intersection of econometrics and statistics with machine learning, and on counterfactual evaluation of measures imposed in the recent pandemic and optimal vaccine allocation across  age-risk groups - at the intersection of econometrics with epidemiology, public health and operations research.

News  2026: 

• February 1: I was nominated Fellow of the International Association for Applied Econometrics

• I will be visiting  University of Bologna, Department of Economics, June 3-10, 2026.