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 - Thiemo Fetzer
Title: Causal Claims (joint work with Prashant Garg (Imperial)).
Abstract: Economic research has experienced a profound shift towards establishing causal relationships using an ever expanding suite of empirical methods. Despite this "credibility revolution," there is limited comprehensive analysis of how these methods and causal claims have evolved across economics subfields. This paper addresses this gap by analyzing over 44,000 NBER and CEPR working papers using a custom large language model to extract structured information on authorship, empirical methods, underlying data and associated causal claims. We use this data to document the empiricisation of economics research with a significant growth in the use of methods like Difference-in-Differences, Instrumental Variables, and Randomized Controlled Trials, alongside an increase in the number of causal claims that are made and evidenced. Yet, nearly 30% of causal claims remain unsupported by rigorous identification strategies. We also observe rising narrative complexity and increased use of private data, raising concerns about transparency, replicability and property rights attached to knowledge goods that are produced using private data. Our findings highlight the evolving landscape of empirical economics, emphasizing the need for continued focus on methodological rigor and data accessibility. This study contributes to a better understanding of research practices in economics and informs efforts to enhance the credibility and transparency of the discipline.