Laura Liu's research interests encompass both macro- and micro-econometrics. Her research is characterized by two primary themes: constructing methods that make better use of newly available granular data for estimation and forecasting, and developing semiparametric methods that balance efficiency and flexibility. She recently works on four topics: (1) panel data and forecasting, (2) panel data and heterogeneous effects, (3) structural macroeconomic models with granular data, and (4) large vector auto-regressions and networks. Her research has been published in Econometrica, Journal of Econometrics, Quantitative Economics, Journal of Business and Economic Statistics, and Journal of Applied Econometrics. She currently serves as an Associate Editor for the Journal of Applied Econometrics and the Journal of Econometric Methods.
Laura received her Ph.D. in Economics from the University of Pennsylvania in 2017. Before coming to Pitt, she worked at the Federal Reserve Board and Indiana University Bloomington.