Understanding Interactions in Social Networks and Committees

Arnab Bhattacharjee, Sean Holly

    Research output: Contribution to journalArticle

    19 Citations (Scopus)

    Abstract

    Abstract While much of the literature on cross-section dependence has focused on estimation of the regression coefficients in the underlying model, estimation and inferences on the magnitude and strength of spillovers and interactions has been largely ignored. At the same time, such inferences are important in many applications, not least because they have structural interpretations and provide useful inferences and structural explanation for the strength of any interactions. In this paper we propose GMM methods designed to uncover underlying (hidden) interactions in social networks and committees. Special attention is paid to the interval censored regression model. Small sample performance is examined through a Monte Carlo study. Our methods are applied to a study of committee decision making within the Bank of England's Monetary Policy Committee.
    Original languageEnglish
    Pages (from-to)23-53
    Number of pages31
    JournalSpatial Economic Analysis
    Volume8
    Issue number1
    DOIs
    Publication statusPublished - 2013

    Fingerprint

    social network
    Social Networks
    monetary policy
    interaction
    Interaction
    Censored Regression
    regression
    Monetary Policy
    cross section
    decision making
    Monte Carlo Study
    Regression Coefficient
    Small Sample
    Regression Model
    bank
    Cross section
    Decision Making
    interpretation
    Interval
    performance

    Cite this

    Bhattacharjee, Arnab ; Holly, Sean. / Understanding Interactions in Social Networks and Committees. In: Spatial Economic Analysis. 2013 ; Vol. 8, No. 1. pp. 23-53.
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    Understanding Interactions in Social Networks and Committees. / Bhattacharjee, Arnab; Holly, Sean.

    In: Spatial Economic Analysis, Vol. 8, No. 1, 2013, p. 23-53.

    Research output: Contribution to journalArticle

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    AB - Abstract While much of the literature on cross-section dependence has focused on estimation of the regression coefficients in the underlying model, estimation and inferences on the magnitude and strength of spillovers and interactions has been largely ignored. At the same time, such inferences are important in many applications, not least because they have structural interpretations and provide useful inferences and structural explanation for the strength of any interactions. In this paper we propose GMM methods designed to uncover underlying (hidden) interactions in social networks and committees. Special attention is paid to the interval censored regression model. Small sample performance is examined through a Monte Carlo study. Our methods are applied to a study of committee decision making within the Bank of England's Monetary Policy Committee.

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