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
Econometrics Seminar - Xiaoxia Shi (Wisconsin)
Title: Testing Inequalities Linear in Nuisance Parameters (with Gregory Cox and Yuya Shimizu) at the econometrics seminar.
Abstract- This paper proposes a new test for inequalities that are linear in possibly partially
identified nuisance parameters, called the generalized conditional chi-squared (GCC)
test. It extends the subvector conditional chi-squared (sCC) test in Cox and Shi (2023,
CS23) to a setting where the nuisance parameter is pre-multiplied by an unknown
and estimable matrix of coefficients. Properly accounting for the estimation noise in
this matrix while maintaining the simplicity of the sCC test is the main innovation
of this paper. [How? New variance formula? Rank condition?] As such, the paper
provides a simple solution to a broad set of problems including subvector inference for
models represented by linear programs, nonparametric instrumental variable models
with discrete regressor and instruments, and linear unconditional moment inequality
models. We also derive a simplified formula for computing the critical value that makes
the computation of the GCC test elementary.