Two-Step Estimation of Incomplete Information Social Interaction Models With Sample Selection

Tadao Hoshino*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


This article considers linear social interaction models under incomplete information that allow for missing outcome data due to sample selection. For model estimation, assuming that each individual forms his/her belief about the other members’ outcomes based on rational expectations, we propose a two-step series nonlinear least squares estimator. Both the consistency and asymptotic normality of the estimator are established. As an empirical illustration, we apply the proposed model and method to National Longitudinal Study of Adolescent Health (Add Health) data to examine the impacts of friendship interactions on adolescents’ academic achievements. We provide empirical evidence that the interaction effects are important determinants of grade point average and that controlling for sample selection bias has certain impacts on the estimation results. Supplementary materials for this article are available online.

Original languageEnglish
Pages (from-to)598-612
Number of pages15
JournalJournal of Business and Economic Statistics
Issue number4
Publication statusPublished - 2019 Oct 2
Externally publishedYes


  • Incomplete information
  • Rational expectation
  • Sample selection
  • Series estimation
  • Social interactions

ASJC Scopus subject areas

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty


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