Big Time Series Analytics for Complex Economic Decisions

Project Information

BigTime

Grant agreement ID: 832671

DOI 10.3030/832671

Project closed

EC signature date 11 April 2019

Start date 1 May 2019

End date 30 April 2021

Funded under

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Total cost € 175 572,48

EU contribution € 175 572,48

Coordinated by

UNIVERSITEIT MAASTRICHT
Netherlands

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Publications

Other (4)

bootUR: An R Package for Bootstrap Unit Root Tests

Author(s): Smeekes, Stephan; Wilms, Ines
Published in: 2020
Publisher: arXiv

Hierarchical regularizers for mixed-frequency vector autoregressions

Author(s): Alain Hecq, Marie Ternes, Ines Wilms
Published in: arXiv, 2021
Publisher: arXiv

Lasso Inference for High-Dimensional Time Series

Author(s): Adamek, Robert; Smeekes, Stephan; Wilms, Ines
Published in: 2020
Publisher: arXiv

Sparse identification and estimation of high-dimensional vector autoregressive moving averages

Author(s): Ines Wilms, Sumanta Basu, Jacob Bien, David S. Matteson
Published in: arXiv, 2019
Publisher: arXiv

Peer reviewed articles (3)

High-dimensional forecasting via interpretable vector autoregression

Author(s): William B. Nicholson, Ines Wilms, Jacob Bien, David S. Matteson
Published in: Journal of Machine Learning Research, 2020, ISSN 1532-4435
Publisher: MIT Press

Volatility spillovers in commodity markets: A large t-vector autoregressive approach

Author(s): Luca Barbaglia, Christophe Croux, Ines Wilms
Published in: Energy Economics, Issue 85, 2020, Page(s) 104555, ISSN 0140-9883
Publisher: Elsevier BV
DOI: 10.1016/j.eneco.2019.104555

Multivariate volatility forecasts for stock market indices

Author(s): Ines Wilms, Jeroen Rombouts, Christophe Croux
Published in: International Journal of Forecasting, Issue 37(2), 2021, Page(s) 484-499, ISSN 0169-2070
Publisher: Elsevier BV
DOI: 10.1016/j.ijforecast.2020.06.012