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Big Time Series Analytics for Complex Economic Decisions

Big Time Series Analytics for Complex Economic Decisions

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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Coordinator

UNIVERSITEIT MAASTRICHT

Address

Minderbroedersberg 4-6
6200 Md Maastricht

Netherlands

Activity type

Higher or Secondary Education Establishments

EU Contribution

€ 175 572,48

Project information

Grant agreement ID: 832671

Status

Ongoing project

  • Start date

    1 May 2019

  • End date

    30 April 2021

Funded under:

H2020-EU.1.3.2.

  • Overall budget:

    € 175 572,48

  • EU contribution

    € 175 572,48

Coordinated by:

UNIVERSITEIT MAASTRICHT

Netherlands