Skip to main navigation Skip to search Skip to main content

Network Estimation from Time Series and Panel Data

  • Julian Burger
  • , Ria H.A. Hoekstra
  • , Alessandra C. Mansueto
  • , Sacha Epskamp

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

23 Scopus citations

Abstract

This chapter discusses how to estimate graphical vector auto-regression (GVAR) network models from time series and panel data. The GVAR model can be used to estimate temporal networks (within-person relationships over time), contemporaneous networks (within-person relationships in the same window of measurement), and between-person networks (relationships between the means of persons in the data). The chapter explains how such network structures can be estimated using the R-packages graphicalVAR, psychonetrics, and mlVAR. The chapter concludes with a discussion of current practical and methodological challenges, including the power of N = 1 networks, heterogeneity, missing data, model assumptions, and the importance of identifying appropriate time scales.

Original languageEnglish
Title of host publicationNetwork Psychometrics with R
Subtitle of host publicationA Guide for Behavioral and Social Scientists, First Edition
PublisherTaylor and Francis
Pages169-192
Number of pages24
ISBN (Electronic)9781000541076
ISBN (Print)9780367628765
DOIs
StatePublished - Jan 1 2022

Fingerprint

Dive into the research topics of 'Network Estimation from Time Series and Panel Data'. Together they form a unique fingerprint.

Cite this