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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

Events

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Econometrics Seminar - Dennis Kristensen (UCL)

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Location: S2.79

Title: Nonparametric Identification and Estimation of Multivariate Transformation Models

Abstract: This paper develops novel results for nonparametric identification of a class of multivariate transformation models. The identification argument imposes very weak restriction on the model and only requires that (i) a set of the covariates are exogenous and that (ii) either instruments or control functions are available to handle the remaing endogenous covariates. The proof is constructive in the sense that it leads to natural estimators of the identified components. We apply the general theory to show identification in a class of multivariate demand models a la Berry-Levinsohn-Pakes under weaker conditions compared to existing ones found in the literature.

There is no working paper version of the paper available.

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