Objective
The financial crisis has highlighted many shortcomings of conventional macro models. Agent-based (AB) models are considered a promising alternative and -thanks also to some important projects financed by the European Commission- Europe is on the frontier of research in the field. However, AB models are rarely taken to the data, but for some ad hoc calibration. Until estimation of AB models become a common practice, they will not get to the center stage of macroeconomics, and policy makers will not rely on them for guidance. The ultimate goal of LAB2DATA is to investigate the most suitable methods for estimating large-scale AB models. To reach this goal the scholar first needs to focus on the specificities of AB models which make estimation hard. These relate to the fact that the aggregate properties of an AB model remain hidden in the complexity of the relations among the different agents and layers (micro and macro) of the system and cannot be directly exploited for estimation.
Then, the scholar needs to focus on the most promising approaches. One is simulated minimum distance (SMD). This has been applied to some small-scale AB models, with few parameters. Another one is sequential Monte Carlo (SMC) methods, also known as particle filtering, which is applied -in simplified settings- in DSGE models.
A first research goal of the project is the application of SMD to larger scale models. A relevant issue here is the ergodicity of the simulation model, which has to be tested in the artificial data generated by the model. A second research goal is the application of SMC methods, which provide probabilistic assessments of the likely evolution of the system and do not rely on ergodicity. As AB models share in many respects the same mathematical structure and complexity of atmospheric models, and these are estimated by SMC methods, the scholar will look at this literature for guidance. A final research goal involves the comparison of the two approaches.
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.
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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.
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.
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.
FP7-PEOPLE-2013-IEF
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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.
Coordinator
OX1 2JD Oxford
United Kingdom
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.