Project description
Robust inference for panel and network data sets
Advanced data availability in economics allows access to richer data sets with increased complexity. However, the fundamental network structure is usually sparse, allowing us to observe only a small subset of all possible prospects. At the same time, the combination of sparsity and a large number of parameters presents challenging econometric problems. The EU-funded PANEDA project will develop robust inference methods for sparse panel and network data sets. The project will establish a mathematical representation of the network allowing the formalisation of asymptotic inference results for growing network sequences. PANEDA will consequently develop new bias correction and robust standard error assessment methods and advance more-close modelling and assessment methods for uninformative data across parameters of interest.
Objective
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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Programme(s)
Funding Scheme
ERC-COG - Consolidator GrantHost institution
OX1 2JD Oxford
United Kingdom