Co-chairs: Alessandra Amendola and Christos Savvas

Description: Separable Hilbert spaces will be considered as a framework for complex elements such as time series, functional data, latent variables associated with text mining models, images, perceptions, or non-precise data.

Main tasks- To investigate new econometric models on Hilbert spaces to handle (possibly mixed) complex data, regression and forecasting problems, the use of general distance covariance in metric spaces, risk measures with new sources of information, inferential methodologies for the defined models, the statistical validity of indicators based on complex data, with particular emphasis on text data, the combination of tools involving functional data and time series, bootstrap and saddlepoint techniques as sampling approximations in Hilbert spaces taking into account the complex dependence structures required in econometrics.