Project description
Enhancing financial decision-making with machine learning and dynamic causal inference
Microeconomics and finance data are essential for economic decision-making by central bankers, investors and policymakers, making their availability crucial. With the support of the Marie Skłodowska Curie Actions programme, the MACROML project will break new ground in the field of macroeconometric modeling by bridging the gap between machine learning and traditional econometrics. It will develop cutting-edge methods for dynamic causal inference analysis of heavy-tailed and highly persistent time series data, which has been largely overlooked in the literature. It will investigate theoretically-valid estimation and inference econometric techniques for general high dimensional time series models, as well as a general methodology for high-dimensional local projection estimators.
Objective
Data lies at the heart of all economic decisions. Everyone — and especially central bankers, investors, and policymakers — processes data when making choices. Thanks to technological innovations, the speed at which (raw) data are generated and shared by businesses, public administrations, and scientific research (among others) have increased exponentially. Large amounts of data bring new opportunities and challenges to econometrics.
The literature on microeconometric methods based on statistical learning techniques has grown substantially over the last decade, yet macroeconometrics literature lacks an understanding of such methods which could be applied to answer causal inference questions. The primary goal of the macroml research project is to put forward theory-driven methods for dynamic causal inference analysis based on models typically used in the macroeconometrics literature, bridging the gap between machine learning and macroeconometric modelling. The key distinction of this project from the state-of-the-art methods is the analysis of heavy-tailed and highly persistent time series data — a critical feature that has been largely overlooked in the literature.
In particular, the research project will investigate:
I. accurate and theoretically-valid estimation and inference econometric techniques for general high-dimensional time series models;
II. a general methodology for high-dimensional local projection estimators which allows studying the dynamic causal relationship between economic time series data.
The project will enlarge policymakers’ toolbox for the analysis of macroeconomics and finance data to assess different dynamic causal hypotheses in a flexible and accurate way, thereby making it highly policy-relevant. In addition, new estimation methods of machine learning time series models will allow practitioners to implement ML techniques for time series data in a data-driven way. The project also will deliver several interesting empirical applications.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- social sciences economics and business economics econometrics
- social sciences economics and business economics macroeconomics
- natural sciences computer and information sciences artificial intelligence machine learning
- social sciences political sciences public administration
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2022-PF-01
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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.
2000 Frederiksberg
Denmark
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.