IDEAS home Printed from https://ideas.repec.org/a/tpr/restat/v101y2019i5p763-776.html
   My bibliography  Save this article

An Empirical Model of Dyadic Link Formation in a Network with Unobserved Heterogeneity

Author

Listed:
  • Andreas Dzemski

    (University of Gothenburg)

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 specification testing and inference with respect to the homophily and reciprocity parameters. The specification test compares observed transitivity to predicted transitivity. All test statistics account for the presence of an incidental parameter by using formulas based on an asymptotic approximation. In an application to favor networks in Indian villages, the specification test detects that the dyadic linking model underestimates the true transitivity of the network.

Suggested Citation

  • Andreas Dzemski, 2019. "An Empirical Model of Dyadic Link Formation in a Network with Unobserved Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 101(5), pages 763-776, December.
    • Handle: RePEc:tpr:restat:v:101:y:2019:i:5:p:763-776
    as

    Download full text from publisher

    File URL: http://www.mitpressjournals.org/doi/pdf/10.1162/rest_a_00805
    Download Restriction: Access to PDF is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. repec:dau:papers:123456789/10840 is not listed on IDEAS
    2. Fernández-Val, Iván & Weidner, Martin, 2016. "Individual and time effects in nonlinear panel models with large N, T," Journal of Econometrics, Elsevier, vol. 192(1), pages 291-312.
    3. Bryan S. Graham, 2015. "Methods of Identification in Social Networks," Annual Review of Economics, Annual Reviews, vol. 7(1), pages 465-485, August.
    4. Koen Jochmans, 2018. "Semiparametric Analysis of Network Formation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(4), pages 705-713, October.
    5. Chernozhukov, Victor & Fernández-Val, Iván & Weidner, Martin, 2024. "Network and panel quantile effects via distribution regression," Journal of Econometrics, Elsevier, vol. 240(2).
    6. Matthew O. Jackson & Tomas Rodriguez-Barraquer & Xu Tan, 2012. "Social Capital and Social Quilts: Network Patterns of Favor Exchange," American Economic Review, American Economic Association, vol. 102(5), pages 1857-1897, August.
    7. Geert Dhaene & Koen Jochmans, 2015. "Split-panel Jackknife Estimation of Fixed-effect Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(3), pages 991-1030.
    8. Kim, Min Seong & Sun, Yixiao, 2016. "BOOTSTRAP AND k-STEP BOOTSTRAP BIAS CORRECTIONS FOR THE FIXED EFFECTS ESTIMATOR IN NONLINEAR PANEL DATA MODELS," Econometric Theory, Cambridge University Press, vol. 32(6), pages 1523-1568, December.
    9. Jinyong Hahn & Whitney Newey, 2004. "Jackknife and Analytical Bias Reduction for Nonlinear Panel Models," Econometrica, Econometric Society, vol. 72(4), pages 1295-1319, July.
    10. Fafchamps, Marcel & Gubert, Flore, 2007. "The formation of risk sharing networks," Journal of Development Economics, Elsevier, vol. 83(2), pages 326-350, July.
    11. Marcel Fafchamps & Flore Gubert, 2007. "Risk Sharing and Network Formation," American Economic Review, American Economic Association, vol. 97(2), pages 75-79, May.
    12. Bryan S. Graham, 2017. "An Econometric Model of Network Formation With Degree Heterogeneity," Econometrica, Econometric Society, vol. 85, pages 1033-1063, July.
    13. Sun, Yixiao & Kim, Min Seong, 2009. "k-step Bootstrap Bias Correction for Fixed Effects Estimators in Nonlinear Panel Models," University of California at San Diego, Economics Working Paper Series qt9gn6n5mr, Department of Economics, UC San Diego.
    14. Leung, Michael P., 2015. "Two-step estimation of network-formation models with incomplete information," Journal of Econometrics, Elsevier, vol. 188(1), pages 182-195.
    15. Hahn, Jinyong & Kuersteiner, Guido, 2011. "Bias Reduction For Dynamic Nonlinear Panel Models With Fixed Effects," Econometric Theory, Cambridge University Press, vol. 27(6), pages 1152-1191, December.
    16. Wayne Yuan Gao, 2017. "Nonparametric Identification in Index Models of Link Formation," Papers 1710.11230, arXiv.org, revised May 2018.
    17. Jinyong Hahn, 1998. "On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects," Econometrica, Econometric Society, vol. 66(2), pages 315-332, March.
    18. Mayer, Adalbert & Puller, Steven L., 2008. "The old boy (and girl) network: Social network formation on university campuses," Journal of Public Economics, Elsevier, vol. 92(1-2), pages 329-347, February.
    19. Ting Yan & Binyan Jiang & Stephen E. Fienberg & Chenlei Leng, 2019. "Statistical Inference in a Directed Network Model With Covariates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 857-868, April.
    20. Geert Dhaene & Koen Jochmans, 2015. "Split-panel Jackknife Estimation of Fixed-effect Models," Review of Economic Studies, Oxford University Press, vol. 82(3), pages 991-1030.
    21. repec:dau:papers:123456789/4392 is not listed on IDEAS
    22. Karyne B. Charbonneau, 2017. "Multiple fixed effects in binary response panel data models," Econometrics Journal, Royal Economic Society, vol. 20(3), pages 1-13, October.
    23. Peter D. Hoff, 2005. "Bilinear Mixed-Effects Models for Dyadic Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 286-295, March.
    24. Stanley Wasserman & Philippa Pattison, 1996. "Logit models and logistic regressions for social networks: I. An introduction to Markov graphs andp," Psychometrika, Springer;The Psychometric Society, vol. 61(3), pages 401-425, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chernozhukov, Victor & Fernández-Val, Iván & Weidner, Martin, 2024. "Network and panel quantile effects via distribution regression," Journal of Econometrics, Elsevier, vol. 240(2).
    2. 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.
    3. David W. Hughes, 2021. "Estimating Nonlinear Network Data Models with Fixed Effects," Boston College Working Papers in Economics 1058, Boston College Department of Economics.
    4. Francesco Bartolucci & Claudia Pigini & Francesco Valentini, 2024. "MCMC conditional maximum likelihood for the two-way fixed-effects logit," Econometric Reviews, Taylor & Francis Journals, vol. 43(6), pages 379-404, July.
    5. Bryan S. Graham, 2019. "Network Data," Papers 1912.06346, arXiv.org.
    6. Bryan S. Graham, 2019. "Network Data," CeMMAP working papers CWP71/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Áureo de Paula, 2020. "Econometric Models of Network Formation," Annual Review of Economics, Annual Reviews, vol. 12(1), pages 775-799, August.
    8. Francesco Bartolucci & Claudia Pigini & Francesco Valentini, 2023. "Conditional inference and bias reduction for partial effects estimation of fixed-effects logit models," Empirical Economics, Springer, vol. 64(5), pages 2257-2290, May.
    9. Francesco Bartolucci & Francesco Valentini & Claudia Pigini, 2023. "Recursive Computation of the Conditional Probability Function of the Quadratic Exponential Model for Binary Panel Data," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 529-557, February.
    10. Xuan Leng & Jiaming Mao & Yutao Sun, 2023. "Debiased inference for dynamic nonlinear models with two-way fixed effects," Papers 2305.03134, arXiv.org, revised Oct 2023.
    11. Alex Centeno, 2022. "A Structural Model for Detecting Communities in Networks," Papers 2209.08380, arXiv.org, revised Oct 2022.
    12. 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.
    13. Jochmans, Koen & Higgins, Ayden, 2022. "Bootstrap inference for fixed-effect models," TSE Working Papers 22-1328, Toulouse School of Economics (TSE), revised Dec 2023.
    14. Mugnier, Martin & Wang, Ao, 2022. "Identification and (Fast) Estimation of Large Nonlinear Panel Models with Two-Way Fixed Effects," The Warwick Economics Research Paper Series (TWERPS) 1422, University of Warwick, Department of Economics.
    15. Candelaria, Luis E. & Ura, Takuya, 2023. "Identification and inference of network formation games with misclassified links," Journal of Econometrics, Elsevier, vol. 235(2), pages 862-891.
    16. Bryan S. Graham, 2017. "An econometric model of network formation with degree heterogeneity," CeMMAP working papers 08/17, Institute for Fiscal Studies.
    17. Weidner, Martin & Zylkin, Thomas, 2021. "Bias and consistency in three-way gravity models," Journal of International Economics, Elsevier, vol. 132(C).
    18. Tadao Hoshino & Daichi Shimamoto & Yasuyuki Todo, 2020. "Accounting for Heterogeneity in Network Formation Behaviour: An Application to Vietnamese SMEs," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(5), pages 1042-1067, October.
    19. Koen Jochmans & Martin Weidner, 2019. "Fixed‐Effect Regressions on Network Data," Econometrica, Econometric Society, vol. 87(5), pages 1543-1560, September.
    20. Gao, Wayne Yuan & Li, Ming & Xu, Sheng, 2023. "Logical differencing in dyadic network formation models with nontransferable utilities," Journal of Econometrics, Elsevier, vol. 235(1), pages 302-324.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:tpr:restat:v:101:y:2019:i:5:p:763-776. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Kelly McDougall (email available below). General contact details of provider: https://direct.mit.edu/journals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.