Econometrics and Data Science
Econometrics and Data Science
The Econometrics and Data Science Research Group covers a wide number of topics within the areas of modern econometric theory and applications, as well as data science in economics. On the econometrics side, the group’s research interests include: the econometrics of networks, panel data econometrics, identification and semiparametric econometrics, macroeconometrics and financial econometrics. On the data science side, the group is interested in, among other topics, machine learning, artificial intelligence, high-dimensional econometrics and text analysis. Such research is often motivated and applied to problems in other fields, including those in industrial organisation, labour economics, political economy, macroeconomics and finance.
The group organises an Econometric seminar that takes place every two weeks on Mondays at 2pm. The group also participates in the CAGE seminar in applied economics, which runs every two weeks on Tuesdays at 2pm, and engages with other seminars in the Department. Students and faculty of the group present their work in progress in two brown bag seminars which run weekly on Tuesdays and Wednesdays at 1pm. The group also co-organises annual workshops, including the Econometrics Workshop, which is a one-day event coupled with an econometrics masterclass.
Our activities
Econometrics Seminar
Monday afternoons
For faculty and PhD students at Warwick and other top-level academic institutions across the world. For a detailed scheduled of speakers please see our upcoming events.
Organisers: Kenichi Nagasawa and Ao Wang
Work in Progress Seminars
Tuesdays and Wednesdays: 1.00-2.00pm
Students and Faculty of the group present their work in progress in two brown bag seminars. For a detailed scheduled of speakers see our upcoming events.
Organiser: Chris Roth
People
Academics
Academics associated with the Reseach Group Name research group are:
Events
CWIP (CAGE Work in Progress) Workshop - Sonia Bhalotra
TITLE: Identifying Managerial Skill (joint with: Ben Weidmann (Harvard), Joe Vecci (Gothenburg), Farah Said (Lahore), David Deming (Harvard))
ABSTRACT: We know that managers matter but we do not know how to prospectively identify good managers. We demonstrate the potential of using repeated random assignment to identify the causal contribution managers make to teams, and the measurable skills associated with this. We randomly assign managers to multiple teams, and predict team performance based on the team’s endowment of productive skill. Some managers consistently cause their teams to exceed predicted performance. Managerial skills are roughly as important to team outcomes as worker productivity. Good managers score higher on measures of allocative skill, and there are no differences in managerial skill across gender, age and ethnicity. We experimentally evaluate different methods of manager selection. People who select into managerial roles are typically not better managers than those appointed by lottery. However, selecting managers based on allocative skill dramatically improves team performance.