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Estimation of a local-aggregate network model with sampled networks

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  • Liu, Xiaodong

Abstract

This work considers the estimation of a network model with sampled networks. Chandrasekhar and Lewis (2011) show that the estimation with sampled networks could be biased due to measurement error induced by sampling and propose a bias correction by restricting the estimation to sampled nodes to avoid measurement error in the regressors. However, measurement error may still exist in the instruments and thus induce the weak instrument problem when the sampling rate is low. For a local-aggregate model, we show that the instrument based on the outdegrees of sampled nodes is free of measurement error and thus remains informative even if the sampling rate is low. Simulation studies suggest that the 2SLS estimator with the proposed instrument works well when the sampling rate is low and the other instruments are weak.

Suggested Citation

  • Liu, Xiaodong, 2013. "Estimation of a local-aggregate network model with sampled networks," Economics Letters, Elsevier, vol. 118(1), pages 243-246.
    • Handle: RePEc:eee:ecolet:v:118:y:2013:i:1:p:243-246
    DOI: 10.1016/j.econlet.2012.10.037
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    1. Bramoullé, Yann & Djebbari, Habiba & Fortin, Bernard, 2009. "Identification of peer effects through social networks," Journal of Econometrics, Elsevier, vol. 150(1), pages 41-55, May.
    2. Banerjee, Abhijit & Jackson, Matthew O. & Duflo, Esther & Chandrasekhar, Arun G., 2012. "The Diffusion of Microfinance," CEPR Discussion Papers 8770, C.E.P.R. Discussion Papers.
    3. Liu, Xiaodong & Lee, Lung-fei, 2010. "GMM estimation of social interaction models with centrality," Journal of Econometrics, Elsevier, vol. 159(1), pages 99-115, November.
    4. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(3), pages 531-542.
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    Citations

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    Cited by:

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    2. Horrace, William C. & Liu, Xiaodong & Patacchini, Eleonora, 2016. "Endogenous network production functions with selectivity," Journal of Econometrics, Elsevier, vol. 190(2), pages 222-232.
    3. Arthur Lewbel & Xi Qu & Xun Tang, 2022. "Estimating Social Network Models with Missing Links," Boston College Working Papers in Economics 1056, Boston College Department of Economics.
    4. Chih‐Sheng Hsieh & Hans van Kippersluis, 2018. "Smoking initiation: Peers and personality," Quantitative Economics, Econometric Society, vol. 9(2), pages 825-863, July.
    5. Chen, Denghui & Kiefer, Hua & Liu, Xiaodong, 2022. "Estimation of discrete choice network models with missing outcome data," Regional Science and Urban Economics, Elsevier, vol. 97(C).
    6. Arun Advani & Bansi Malde, 2018. "Credibly Identifying Social Effects: Accounting For Network Formation And Measurement Error," Journal of Economic Surveys, Wiley Blackwell, vol. 32(4), pages 1016-1044, September.
    7. Chih-Sheng Hsieh & Stanley I. M. Ko & Jaromír Kovářík & Trevon Logan, 2018. "Non-Randomly Sampled Networks: Biases and Corrections," NBER Working Papers 25270, National Bureau of Economic Research, Inc.
    8. Arun Advani & Bansi Malde, 2018. "Methods to identify linear network models: a review," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 154(1), pages 1-16, December.
    9. 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.
    10. Michael D. König & Xiaodong Liu & Yves Zenou, 2019. "R&D Networks: Theory, Empirics, and Policy Implications," The Review of Economics and Statistics, MIT Press, vol. 101(3), pages 476-491, July.
    11. Zhu, Xuening & Huang, Danyang & Pan, Rui & Wang, Hansheng, 2020. "Multivariate spatial autoregressive model for large scale social networks," Journal of Econometrics, Elsevier, vol. 215(2), pages 591-606.
    12. Lina Zhang, 2020. "Spillovers of Program Benefits with Missing Network Links," Papers 2009.09614, arXiv.org, revised Apr 2023.
    13. Firmin Doko Tchatoka & Robert Garrard & Virginie Masson, 2017. "Testing for Stochastic Dominance in Social Networks," School of Economics and Public Policy Working Papers 2017-02, University of Adelaide, School of Economics and Public Policy.
    14. Arun Advani & Bansi Malde, 2014. "Empirical methods for networks data: social effects, network formation and measurement error," IFS Working Papers W14/34, Institute for Fiscal Studies.
    15. Hsieh, Chih-Sheng & Lin, Xu, 2017. "Gender and racial peer effects with endogenous network formation," Regional Science and Urban Economics, Elsevier, vol. 67(C), pages 135-147.

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    More about this item

    Keywords

    Social networks; Local-average models; Local-aggregate models; Sampling of networks; Weak instruments;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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