econet: An R Package for Parameter-Dependent Network Centrality Measures

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 netw...

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Main Authors: Marco Battaglini, Valerio Leone Sciabolazza, Eleonora Patacchini, Sida Peng
Format: Article
Language:English
Published: Foundation for Open Access Statistics 2022-04-01
Series:Journal of Statistical Software
Subjects:
Online Access:https://www.jstatsoft.org/index.php/jss/article/view/3508
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author Marco Battaglini
Valerio Leone Sciabolazza
Eleonora Patacchini
Sida Peng
author_facet Marco Battaglini
Valerio Leone Sciabolazza
Eleonora Patacchini
Sida Peng
author_sort Marco Battaglini
collection DOAJ
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).
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spelling doaj.art-62b9d68ba0414e18a8602ba327bde2d52023-06-01T18:48:04ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602022-04-0110213010.18637/jss.v102.i083356econet: An R Package for Parameter-Dependent Network Centrality MeasuresMarco Battaglini0https://orcid.org/0000-0001-9690-0721Valerio Leone Sciabolazza1https://orcid.org/0000-0003-2537-3084Eleonora Patacchini2https://orcid.org/0000-0002-3510-2969Sida Peng3Cornell University, EIEF, NBERSapienza University of Rome, CEISCornell University, EIEFMicrosoft ResearchThe 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).https://www.jstatsoft.org/index.php/jss/article/view/3508network econometricsheterogeneous peer effectsendogenous network formationleast-square estimatorsmaximum likelihood estimatorsr
spellingShingle Marco Battaglini
Valerio Leone Sciabolazza
Eleonora Patacchini
Sida Peng
econet: An R Package for Parameter-Dependent Network Centrality Measures
Journal of Statistical Software
network econometrics
heterogeneous peer effects
endogenous network formation
least-square estimators
maximum likelihood estimators
r
title econet: An R Package for Parameter-Dependent Network Centrality Measures
title_full econet: An R Package for Parameter-Dependent Network Centrality Measures
title_fullStr econet: An R Package for Parameter-Dependent Network Centrality Measures
title_full_unstemmed econet: An R Package for Parameter-Dependent Network Centrality Measures
title_short econet: An R Package for Parameter-Dependent Network Centrality Measures
title_sort econet an r package for parameter dependent network centrality measures
topic network econometrics
heterogeneous peer effects
endogenous network formation
least-square estimators
maximum likelihood estimators
r
url https://www.jstatsoft.org/index.php/jss/article/view/3508
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AT sidapeng econetanrpackageforparameterdependentnetworkcentralitymeasures