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 Lunchtime Workshop - Robert Akerlof
Title: Narratives and the Economics of the Family
Abstract: We argue that families adopt stories and that different stories give rise to different patterns of behavior. We build a theoretical model, focusing on two common, competing stories, which we term the “protector narrative” and “fulfillment narrative.” Our model makes predictions regarding the bundle of behaviors associated with each of these narratives; it also makes predictions regarding the narratives families will choose to adopt. We show that the protector narrative gives rise to a type of “traditional” family with distinct gender roles: men are breadwinners, are authoritarian towards women and children, and are expected to be “tough”; women avoid work when feasible, and are not expected to be “tough.” Because of role differentiation, it is important to be part of a family. The fulfillment narrative gives rise to a type of “modern” family in which gender roles are less distinct, both men and women work, and marriages are based, to a greater extent, on romantic love.
Organiser: Christopher Roth