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An empirical model of dyadic link formation in a network with unobserved heterogeneity

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  • Dzemski, Andreas

    (Department of Economics, School of Business, Economics and Law, Göteborg University)

Abstract

I study a dyadic linking model in which agents form directed links that exhibit homophily and reciprocity. A fixed effect approach accounts for unobserved sources of degree heterogeneity. I consider inference with respect to homophily preferences and a reciprocity parameter, as well as a test of model specification. The specification test compares observed transitivity to the transitivity predicted by the dyadic linking model. My test statistics are robust to the incidental parameter problem (Neyman and Scott 1948). This is accomplished by using analytical formulas that approximate the effect of the incidental parameter on the bias and the variance of the test statistics. The approximation is justified under dense large network asymptotics. In an application to favor networks in Indian villages, the model specification test detects that the dyadic linking model underestimates the true transitivity of the network.

Suggested Citation

  • Dzemski, Andreas, 2017. "An empirical model of dyadic link formation in a network with unobserved heterogeneity," Working Papers in Economics 698, University of Gothenburg, Department of Economics, revised Apr 2018.
  • Handle: RePEc:hhs:gunwpe:0698
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    File URL: http://hdl.handle.net/2077/52093
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    References listed on IDEAS

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    Cited by:

    1. Victor Chernozhukov & Ivan Fernandez-Val & Martin Weidner, 2018. "Network and panel quantile effects via distribution regression," CeMMAP working papers CWP21/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Weidner, Martin & Zylkin, Thomas, 2021. "Bias and consistency in three-way gravity models," Journal of International Economics, Elsevier, vol. 132(C).
    3. Chih-Sheng Hsieh & Michael D. Konig & Xiaodong Liu, 2022. "A Structural Model for the Coevolution of Networks and Behavior," The Review of Economics and Statistics, MIT Press, vol. 104(2), pages 355-367, May.
    4. Áureo de Paula, 2020. "Econometric Models of Network Formation," Annual Review of Economics, Annual Reviews, vol. 12(1), pages 775-799, August.
    5. Braun, Martin & Verdier, Valentin, 2023. "Estimation of spillover effects with matched data or longitudinal network data," Journal of Econometrics, Elsevier, vol. 233(2), pages 689-714.
    6. Gao, Wayne Yuan, 2020. "Nonparametric identification in index models of link formation," Journal of Econometrics, Elsevier, vol. 215(2), pages 399-413.
    7. Chen, Mingli & Fernández-Val, Iván & Weidner, Martin, 2021. "Nonlinear factor models for network and panel data," Journal of Econometrics, Elsevier, vol. 220(2), pages 296-324.
    8. Bryan S. Graham, 2019. "Network Data," NBER Working Papers 26577, National Bureau of Economic Research, Inc.
    9. Ida Johnsson & Hyungsik Roger Moon, 2017. "Estimation of Peer Effects in Endogenous Social Networks: Control Function Approach," Papers 1709.10024, arXiv.org, revised Jul 2019.
    10. Andrin Pelican & Bryan S. Graham, 2020. "An Optimal Test for Strategic Interaction in Social and Economic Network Formation between Heterogeneous Agents," NBER Working Papers 27793, National Bureau of Economic Research, Inc.
    11. Wayne Yuan Gao, 2017. "Nonparametric Identification in Index Models of Link Formation," Papers 1710.11230, arXiv.org, revised May 2018.
    12. Bryan S. Graham, 2019. "Network Data," CeMMAP working papers CWP71/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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    More about this item

    Keywords

    Network formation; fixed effects; incidental parameter problem; transitive structure; favor networks;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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