PANEDA
Financiado
High Dimensional Inference for Panel and Network Data
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. ho...
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.
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Duración del proyecto: 79 meses
Fecha Inicio: 2018-12-18
Fecha Fin: 2025-07-31
Fecha Fin: 2025-07-31
Línea de financiación: concedida
El organismo H2020 notifico la concesión del proyecto el día 2018-12-18
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
ERC-2018-COG:
ERC Consolidator Grant
Cerrada
hace 6 años
Presupuesto
El presupuesto total del proyecto asciende a
1M€
Líder del proyecto
THE CHANCELLOR MASTERS AND SCHOLARS OF THE UN...
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Perfil tecnológico
TRL
4-5
Perfil tecnológico
TRL
4-5
697.27K€ |
Participante