Objective
Unobserved heterogeneity and endogeneity are prevalent notions throughout econometrics. Most of the literature focuses on scalar unobserved heterogeneity. It implies strong restrictions on the heterogeneity of the behaviour of economic agents. This is the case in a binary treatment effect model where scalar unobserved heterogeneity and additive separability of the index in the selection equation are equivalent to the restrictive monotonicity assumption. Nonparametric random coefficients models allow for multiple sources of unobserved heterogeneity and are in line with structural economics. They are also benchmark nonseparable models and can be generalized in various ways. Due to unobserved heterogeneity, but also simultaneity or error in variables, structural models usually involve as well endogenous regressors.
Nonparametric models of unobserved heterogeneity and estimation by instrumental variables usually give rise to ill-posed inverse problems. High-dimensional methods are a new set of tools that are increasingly popular in econometrics and allow handling new data configurations with many more potential regressors than observations. They are based on convex relaxation, linear or conic programming ideas, or MCMC algorithms. When the model is well approximated by a parsimonious model where many coefficients are zero they can usually estimate the parameter as well as an oracle who would know the best sparse approximation. They also offer new tools for adaptive nonparametric estimation. Some recent developments are concerned with hidden structured sparsity (structural breakpoints or other patterns other than zeros). This research proposal is on the development of a general framework and new inference tools for flexible models – nonparametric or high-dimensional – with multiple sources of unobserved heterogeneity and endogeneity in various models from economics, in particular: programme evaluation, consumer demand, demand for differentiated products, games, etc.
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: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
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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.
ERC-2013-StG
See other projects for this call
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.
Host institution
31080 Toulouse
France
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.