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:
Eric Renault
Co-ordinator
Mingli Chen
Deputy Co-ordinator
Events
Wed 8 May, '24- |
CAGE-AMES Workshop - Adam Di Lizia (PGR)S2.79Title: Social Influence in Online Reviews: Evidence from the Steam Store Abstract: How good are reviews as signals of product quality for consumers? Using a data-set derived from the popular Steam gaming platform I investigate the ‘priming’ of quality judgements as based on pre-existing consumer assessments. A policy reform on Steam in 2019 changed the average level of exposure to previous consumer quality ratings, with this randomly occurring within a game and reviewer’s life cycle. I find that removing the exposure of a reviewer to a product’s average rating leads to a 35% drop in the dependency of their review on such a rating. This is not driven by selection effects, and is robust to a wide range of alternate specifications and measures. The effect is heavily asymmetric: negativity compounds to inflate the gap between poorly-rated and well rated games. This is driven by users who are less experienced both within and across games. Finally, using estimates of owner data, I run a simple structural model of game choice based on rating. A 1% increase to product rating is equivalent to a 2.5 dollar sale price reduction, suggesting this effect has large implications for buyers and sellers.
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Wed 5 Jun, '24- |
CAGE-AMES Workshop - to be advisedS0.09Title to be advised. |