Network Data
Many economic activities are embedded in networks: sets of agents and the (often) rivalrous relationships connecting them to one another. Input sourcing by firms, interbank lending, scientific research, and job search are four examples, among many, of networked economic activities. Motivated by the premise that networks' structures are consequential, this chapter describes econometric methods for analyzing them. I emphasize (i) dyadic regression analysis incorporating unobserved agent-specific heterogeneity and supporting causal inference, (ii) techniques for estimating, and conducting inference on, summary network parameters (e.g., the degree distribution or transitivity index); and (iii) empirical models of strategic network formation admitting interdependencies in preferences. Current research challenges and open questions are also discussed.
Code (0)
등록된 구현이 없습니다.
Tasks
Causal InferenceregressionSimilar Papers 제목 키워드 기반
Proceedings of the 3rd Italian Conference on Big Data and Data Science (ITADATA2024)
Proceedings of the 3rd Italian Conference on Big Data and Data Science (ITADATA2024), held in Pisa, Italy, September 17-19, 2024. The Italian Conference on Big Data and Data Science (ITADATA2024) is the annual event supp…
A Random Sample Partition Data Model for Big Data Analysis
Big data sets must be carefully partitioned into statistically similar data subsets that can be used as representative samples for big data analysis tasks. In this paper, we propose the random sample partition (RSP) data…
Data Lakes, Clouds and Commons: A Review of Platforms for Analyzing and Sharing Genomic Data
Data commons collate data with cloud computing infrastructure and commonly used software services, tools and applications to create biomedical resources for the large-scale management, analysis, harmonization, and sharin…
Cloud ComputingManagementOnedata4Sci: Life science data management solution based on Onedata
Life-science experimental methods generate vast and ever-increasing volumes of data, which provide highly valuable research resources. However, management of these data is nontrivial and applicable software solutions are…
ManagementOpenDataLab: Empowering General Artificial Intelligence with Open Datasets
The advancement of artificial intelligence (AI) hinges on the quality and accessibility of data, yet the current fragmentation and variability of data sources hinder efficient data utilization. The dispersion of data sou…
Diversity