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Score-driven TEnsor Autoregressive DYnamical models

Project description

New non-linear and dynamic models to better analyse data structures

A data revolution has occurred in the last decade. In many fields, new models are needed to analyse multidimensional arrays of data called tensors. Current econometric models aren’t suitable for such data. They either simplify the problem too much or use models that are too static. The EU-funded STEADY project aims to develop new tensor models that account for the non-linear and dynamic features of economic data. It will therefore focus on developing a general class of dynamic time-series models as well as new tensor-based compression techniques for many economic time series. The project’s contributions will be applied to policy-relevant questions for central banks and financial regulators.

Objective

The last decade has been characterised by a data revolution. In economics and elsewhere (physics, machine learning, biology, imaging, statistics) ever more data structures emerge that require new models suited for analysing multidimensional arrays of data, so called tensors (e.g. data of firm exposures (dimension 1) to other firms (dim.2) over time (dim.3) in different markets (dim.4)). Adequate econometric models for such data are currently largely lacking. They either simplify the problem to the 2-dimensional setting, or use models that are too static to account for rapid changes in economic conditions. STEADY fills this gap by developing new tensor models that account for the typical non-linear and dynamic features of economic data. STEADY concentrates on two main contributions: developing a general class of dynamic time-series models (tensor score-driven time-varying parameter models), and developing new tensor-based compression techniques for many economic time series (tensor dynamic factor models). The models developed will also be applicable in related fields. Both contributions of STEADY are applied to policy relevant questions for central banks and financial regulators, including forecasting multi-country, multi-market interest rate term structures for the evaluation of monetary policy effectiveness, and nowcasting multi-country economic activity in the heterogeneous European context. This is done by a close cooperation between the principal researcher, experts at VUA (host), and the European Central Bank (ECB). A secondment to ECB is key to the project, such that methodology, application, and implementation can be developed as a joint, cross-disciplinary effort between university and policymakers.

Fields of science (EuroSciVoc)

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Programme(s)

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Topic(s)

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Funding Scheme

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MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) H2020-MSCA-IF-2019

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Coordinator

STICHTING VU
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 175 572,48
Address
DE BOELELAAN 1105
1081 HV Amsterdam
Netherlands

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Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 175 572,48
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