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BigTime SIGNED

Big Time Series Analytics for Complex Economic Decisions

Total Cost €

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EC-Contrib. €

0

Partnership

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Project "BigTime" data sheet

The following table provides information about the project.

Coordinator
UNIVERSITEIT MAASTRICHT 

Organization address
address: Minderbroedersberg 4-6
city: MAASTRICHT
postcode: 6200 MD
website: http://www.maastrichtuniversity.nl

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Netherlands [NL]
 Total cost 175˙572 €
 EC max contribution 175˙572 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2018
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2019
 Duration (year-month-day) from 2019-05-01   to  2021-04-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITEIT MAASTRICHT NL (MAASTRICHT) coordinator 175˙572.00

Map

 Project objective

Big time series data are commonplace in economics. Their variety and sheer size provide nearly endless opportunities to improve economic decision making at European governments, companies and universities: amongst others, internet search data could shed light on consumer sentiment, social media provide opportunities for improving economic policy analysis, and high-frequency volatility data could be informative for financial risk analysis.

While the expansion of these Big Data sources bring possibilities, it also raises ever-increasing statistical challenges since novel methods (for instance, 'penalized' methods) are needed to estimate high-dimensional models containing many parameters. The development of such methods has flourished in the statistical learning community, but they are not geared towards the specificities of economic time series. Econometric time series models typically differ from traditional statistical models in that they require (i) an accurate assessment of the certainty of the economic findings and predictions, (ii) a description of how the economy responds, over time, to exogenous shocks, and (iii) an identification strategy that maps the observed data to the relevant economic parameters of interest. The proposal builds a partnership between econometrics, statistics and machine learning with the aim of addressing these three econometric objectives. It develops statistical learning methods for (i) honest uncertainty quantification (inference), (ii) interpretable economic impulse response functions analysis and (iii) identification of high-dimensional time series models. The suitability of the developed Big Time Series methods is demonstrated for economic applications including financial risk analysis and macro-economic policy analysis.

As such, the proposal provides a Big Time Series Analytics toolbox to modern empirical economists that aims to support and improve economic decision making in big, dynamic and complex time series problems.

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The information about "BIGTIME" are provided by the European Opendata Portal: CORDIS opendata.

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