Finite Sample Behavior of MLE in Network Autocorrelation Models

  • Michele La RoccaEmail author
  • Giovanni C. Porzio
  • Maria Prosperina Vitale
  • Patrick Doreian
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


This work evaluates the finite sample behavior of ML estimators in network autocorrelation models, a class of auto-regressive models studying the network effect on a variable of interest. Through an extensive simulation study, we examine the conditions under which these estimators are normally distributed in the case of finite samples. The ML estimators of the autocorrelation parameter have a negative bias and a strongly asymmetric sampling distribution, especially for high values of the network effect size and the network density. In contrast, the estimator of the intercept is positively biased but with an asymmetric sampling distribution. Estimators of the other regression parameters are unbiased, with heavy tails in presence of non-normal errors. This occurs not only in randomly generated networks but also in well-established network structures.


Network effect model Density Network topology Non-normal distribution 


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Michele La Rocca
    • 1
    Email author
  • Giovanni C. Porzio
    • 2
  • Maria Prosperina Vitale
    • 1
  • Patrick Doreian
    • 3
    • 4
  1. 1.Department of Economics and StatisticsUniversity of SalernoFiscianoItaly
  2. 2.Department of Economics and LawUniversity of Cassino and Southern LazioCassinoItaly
  3. 3.Faculty of Social SciencesUniversity of LjubljanaLjubljanaSlovenia
  4. 4.Department of SociologyUniversity of PittsburghPittsburghUSA

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