econet: An R Package for Parameter-Dependent Network Centrality Measures
Dublin Core
Title
econet: An R Package for Parameter-Dependent Network Centrality Measures
Subject
network econometrics, heterogeneous peer effects, endogenous network formation,
least-square estimators, maximum likelihood estimators, R
least-square estimators, maximum likelihood estimators, R
Description
The R package econet provides methods for estimating parameter-dependent network
centrality measures with linear-in-means models. Both nonlinear least squares and maximum likelihood estimators are implemented. The methods allow for both link and node
heterogeneity in network effects, endogenous network formation and the presence of unconnected nodes. The routines also compare the explanatory power of parameter-dependent
network centrality measures with those of standard measures of network centrality. Benefits and features of the econet package are illustrated using data from Battaglini and
Patacchini (2018) and Battaglini, Leone Sciabolazza, and Patacchini (2020
centrality measures with linear-in-means models. Both nonlinear least squares and maximum likelihood estimators are implemented. The methods allow for both link and node
heterogeneity in network effects, endogenous network formation and the presence of unconnected nodes. The routines also compare the explanatory power of parameter-dependent
network centrality measures with those of standard measures of network centrality. Benefits and features of the econet package are illustrated using data from Battaglini and
Patacchini (2018) and Battaglini, Leone Sciabolazza, and Patacchini (2020
Creator
Marco Battaglini
Source
https://www.jstatsoft.org/article/view/v102i08
Publisher
Cornell University, EIEF, NBER
Date
April 2022
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Marco Battaglini, “econet: An R Package for Parameter-Dependent Network Centrality Measures,” Repository Horizon University Indonesia, accessed April 4, 2025, https://repository.horizon.ac.id/items/show/8254.