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Estimation of a local-aggregate network model with sampled networks

Author

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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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    References listed on IDEAS

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    1. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," Review of Economic Studies, Oxford University Press, vol. 60(3), pages 531-542.
    2. 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.
    3. Banerjee, Abhijit & Chandrasekhar, Arun G & Duflo, Esther & Jackson, Matthew O., 2012. "The Diffusion of Microfinance," CEPR Discussion Papers 8770, C.E.P.R. Discussion Papers.
    4. 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.
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    Citations

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    Cited by:

    1. König, Michael & Liu, Xiaodong & Zenou, Yves, 2014. "R&D Networks: Theory, Empirics and Policy Implications," CEPR Discussion Papers 9872, C.E.P.R. Discussion Papers.
    2. Tiziano Arduini & Eleonora Patacchini & Edoardo Rainone, 2014. "Identification and Estimation of Outcome Response with Heterogeneous Treatment Externalities," EIEF Working Papers Series 1407, Einaudi Institute for Economics and Finance (EIEF), revised Sep 2014.
    3. Horrace, William C. & Liu, Xiaodong & Patacchini, Eleonora, 2016. "Endogenous network production functions with selectivity," Journal of Econometrics, Elsevier, vol. 190(2), pages 222-232.
    4. Firmin Doko Tchatoka & Robert Garrard & Virginie Masson, 2017. "Testing for Stochastic Dominance in Social Networks," School of Economics Working Papers 2017-02, University of Adelaide, School of Economics.
    5. repec:zur:econwp:142 is not listed on IDEAS
    6. 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.
    7. repec:eee:regeco:v:67:y:2017:i:c:p:135-147 is not listed on IDEAS
    8. repec:spr:sjecst:v:154:y:2018:i:1:d:10.1186_s41937-017-0011-x is not listed on IDEAS
    9. Chih-Sheng Hsieh & Hans van Kippersluis, 2015. "Smoking Initiation: Peers and Personality," Tinbergen Institute Discussion Papers 15-093/V, Tinbergen Institute.

    More about this item

    Keywords

    Social networks; Local-average models; Local-aggregate models; Sampling of networks; Weak instruments;

    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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