Explore the words cloud of the PANEDA project. It provides you a very rough idea of what is the project "PANEDA" about.
The following table provides information about the project.
UNIVERSITY COLLEGE LONDON
|Coordinator Country||United Kingdom [UK]|
|Total cost||1˙478˙831 €|
|EC max contribution||1˙478˙831 € (100%)|
1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
|Duration (year-month-day)||from 2019-08-01 to 2024-07-31|
Take a look of project's partnership.
|1||UNIVERSITY COLLEGE LONDON||UK (LONDON)||coordinator||1˙478˙831.00|
Improved data availability in Economics provides access to richer datasets with increased complexity. There is regularly a network aspect to the data, whenever outcomes are observed for matches of different economic units (e.g. households, individuals, firms, products, markets). Such observations include, e.g., wages for workers in firms, academic achievement for students taught by teachers in schools, and purchasing decisions for consumers in stores. The underlying network structure is often sparse, because we only observe a small subset of all possible matches, say between workers and firms. In addition, we aim to estimate models with many parameters, for example to control for and to estimate unobserved heterogeneity of economic units by including (e.g. worker and firm specific) fixed effects.
The combination of sparsity of the underlying network structure and a large number of parameters in the model creates challenging Econometric problems. In particular, there is a serious gap between empirical practice, where applied researchers regularly use such sparse network datasets, and the theoretical justifications for those inference methods that are based on classic data structures (cross-sectional, time-series, and panel data) that do not account for the sparsity aspect of the data.
The goal of this research project is to develop robust inference methods for such sparse panel and network datasets. This requires to establish a mathematical representation of the network that allows to formalize asymptotic inference results for sequences of growing networks. Subsequently, new bias correction and robust standard error estimation methods will be developed that account for the sparsity structure of the data. I will also advance more parsimonious modeling and estimation approaches (e.g. grouped heterogeneity or empirical Bayes) for situations where the data are otherwise uninformative for the parameters of interest.
Are you the coordinator (or a participant) of this project? Plaese send me more information about the "PANEDA" project.
For instance: the website url (it has not provided by EU-opendata yet), the logo, a more detailed description of the project (in plain text as a rtf file or a word file), some pictures (as picture files, not embedded into any word file), twitter account, linkedin page, etc.
Send me an email (firstname.lastname@example.org) and I put them in your project's page as son as possible.
Thanks. And then put a link of this page into your project's website.
The information about "PANEDA" are provided by the European Opendata Portal: CORDIS opendata.