Congratulations to CREST researchers winning five ANR Grants!

Name: Sylvain CATHERINE (University of Pennsylvania) “t.b.a.”
Start: 2024-05-13T12:15:00+02:00
End: 2024-05-13T13:30:00+02:00

Leave a comment

Crest and Genes researchers contribute to selected ANR 2021 projects:

– Medialex with Etienne Ollion proposes to develop original numerical methods, combining social science skills, both quantitative and qualitative, with skills in modelling and automatic language processing.

– MomBay (Moment Conditions Models and Bayesian Inference for Policy Evaluation) with Anna Simoni aims to design econometric methodologies that improve the quality of the information extracted from the newly available data in order to understand the effect of potential economy policies on: the societies, the interactions among individuals, and the labour market structure and mutations. These questions about the society will motivate and guide the development of our theoretical and computational contributions.

– MleForRisk with Jean-Michel Zakoian aims to provide better understanding of the usefulness of the combination of Machine Learning and econometrics for financial risk measurement. MLEforRisk is a multi-disciplinary project in the fields of Finance and Financial Econometrics, which brings together senior and junior researchers in business, economics, and applied mathematics.

– EPIVASCAGE (EPIdemiology of VASCular AGEing) with Nicolas Chopin is a 4-year PRC project aiming to examine the association of baseline and vascular ageing progression for incident cardiovascular disease (CVD) and mortality in the community.

– DREAMES (Numerical methods for decision: dynamic preferences and multivariate risks) with Caroline Hillairet and Wissal Sabbagh. In presence of abrupt (financial crisis or epidemics) or long-term (environmental or demographic) changes, one needs to use dynamic tools, to detect such changes from observable data, and to re- estimate models and risk quantification parameters, based on a dynamic and long-term view. The main objectives of this project are optimal detection of tendency changes in the environment, and optimization of economic actors’ decisions using dynamic preference criteria.