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 - Lorenzo Magnolfi (Wisconsin)
Title: Market Counterfactuals with Nonparametric Supply: An ML/AI Approach (with Harold Chiang, Jack Collison, and Chris Sullivan).
Abstract: This paper develops a flexible approach to perform market counterfactuals using machine learning methods and nonparametric structure from economics. While standard structural methods rely on restrictive assumptions about firm conduct and cost, we propose a data-driven framework that relaxes these constraints when rich market data are available. Building on the identification results of Berry and Haile (2014) we develop a nonparametric model of supply that nests traditional conduct specifications while allowing for more complex competitive interactions. We adapt the Variational Method of Moments (VMM) (Bennett and Kallus, 2023) to estimate this flexible model, addressing the endogeneity of market shares and the high dimensionality of the problem. Our approach enables a wide range of counterfactual exercises including tax policy analysis, product regulation, and merger simulation. Monte Carlo simulations demonstrate that our method substantially outperforms standard approaches; applied to the American Airlines-US Airways merger, our method produces more accurate post-merger price predictions.