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).

Econet. an R package for parameter-dependent network centrality measures / Battaglini, Marco; LEONE SCIABOLAZZA, Valerio; Patacchini, Eleonora; Peng, Sida. - In: JOURNAL OF STATISTICAL SOFTWARE. - ISSN 1548-7660. - 102:8(2022), pp. 1-30. [10.18637/jss.v102.i08]

Econet. an R package for parameter-dependent network centrality measures

Valerio Leone Sciabolazza
;
2022

Abstract

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).
2022
network econometrics; heterogeneous peer effects; endogenous network formation; least-square estimators; maximum likelihood estimators; R
01 Pubblicazione su rivista::01a Articolo in rivista
Econet. an R package for parameter-dependent network centrality measures / Battaglini, Marco; LEONE SCIABOLAZZA, Valerio; Patacchini, Eleonora; Peng, Sida. - In: JOURNAL OF STATISTICAL SOFTWARE. - ISSN 1548-7660. - 102:8(2022), pp. 1-30. [10.18637/jss.v102.i08]
File allegati a questo prodotto
File Dimensione Formato  
Sciabolazza_Econet_2022.pdf

accesso aperto

Tipologia: Documento in Post-print (versione successiva alla peer review e accettata per la pubblicazione)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 739.98 kB
Formato Adobe PDF
739.98 kB Adobe PDF

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1630646
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact