Project description
Improving economic decision-making
The expansion of Big Data sources brings numerous opportunities to the field of economics. At the same time, it increases statistical challenges as methods that can estimate high-dimensional models containing many parameters are needed. Such models can be found in the statistical learning community, but they are not suited to economic time series. The EU-funded BigTime project, drawing upon econometrics, statistics and machine learning, aims to develop statistical learning methods that provide honest uncertainty quantification, interpretable economic impulse response function analysis and the identification of high-dimensional time series models. The project will develop a big time series analytics toolbox that will support and improve economic decision-making in big, dynamic and complex time series problems.
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.
Fields of science
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- social scienceseconomics and businesseconomicseconometrics
- natural sciencescomputer and information sciencesinternet
- natural sciencescomputer and information sciencesdata sciencebig data
- natural sciencesmathematicsapplied mathematicsstatistics and probability
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programme(s)
Funding Scheme
MSCA-IF-EF-ST - Standard EFCoordinator
6200 MD Maastricht
Netherlands