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GMM identification and estimation of peer effects in a system of simultaneous equations

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  • Xiaodong Liu

    (University of Colorado Boulder)

Abstract

This paper considers the identification and estimation of network models with agents interacting in multiple activities. We establish the model identification using both linear and quadratic moment conditions. The quadratic moment conditions exploit the correlation of individual decisions within and across different activities, and provide an additional channel to identify peer effects. Combining linear and quadratic moment conditions, we propose a general GMM framework for the estimation of simultaneous equations network models. The GMM estimator improves the asymptotic efficiency of the existing IV-based linear estimators in the literature. Simulation experiments show that the GMM estimator performs well in finite samples.

Suggested Citation

  • Xiaodong Liu, 2020. "GMM identification and estimation of peer effects in a system of simultaneous equations," Journal of Spatial Econometrics, Springer, vol. 1(1), pages 1-27, December.
  • Handle: RePEc:spr:jospat:v:1:y:2020:i:1:d:10.1007_s43071-020-0001-4
    DOI: 10.1007/s43071-020-0001-4
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    1. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
    2. Bryan S. Graham, 2008. "Identifying Social Interactions Through Conditional Variance Restrictions," Econometrica, Econometric Society, vol. 76(3), pages 643-660, May.
    3. Edward L. Glaeser & Bruce Sacerdote & José A. Scheinkman, 1996. "Crime and Social Interactions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 111(2), pages 507-548.
    4. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492, National Bureau of Economic Research, Inc.
    5. 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.
    6. Donald, Stephen G & Newey, Whitney K, 2001. "Choosing the Number of Instruments," Econometrica, Econometric Society, vol. 69(5), pages 1161-1191, September.
    7. Steven N. Durlauf & Hisatoshi Tanaka, 2008. "Understanding Regression Versus Variance Tests For Social Interactions," Economic Inquiry, Western Economic Association International, vol. 46(1), pages 25-28, January.
    8. White,Halbert, 1996. "Estimation, Inference and Specification Analysis," Cambridge Books, Cambridge University Press, number 9780521574464, January.
    9. Ethan Cohen‐Cole & Xiaodong Liu & Yves Zenou, 2018. "Multivariate choices and identification of social interactions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(2), pages 165-178, March.
    10. Bekker, Paul A, 1994. "Alternative Approximations to the Distributions of Instrumental Variable Estimators," Econometrica, Econometric Society, vol. 62(3), pages 657-681, May.
    11. Xiaodong Liu, 2014. "Identification and Efficient Estimation of Simultaneous Equations Network Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(4), pages 516-536, October.
    12. Lee, Lung-fei, 2007. "The method of elimination and substitution in the GMM estimation of mixed regressive, spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 140(1), pages 155-189, September.
    13. Kelejian, Harry H. & Prucha, Ingmar R., 2004. "Estimation of simultaneous systems of spatially interrelated cross sectional equations," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 27-50.
    14. Fei Jin & Lung-fei Lee, 2013. "Generalized Spatial Two Stage Least Squares Estimation of Spatial Autoregressive Models with Autoregressive Disturbances in the Presence of Endogenous Regressors and Many Instruments," Econometrics, MDPI, vol. 1(1), pages 1-44, May.
    15. 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.
    16. Hansen, Christian & Hausman, Jerry & Newey, Whitney, 2008. "Estimation With Many Instrumental Variables," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 398-422.
    17. Xiaodong Liu & Paulo Saraiva, 2019. "GMM estimation of spatial autoregressive models in a system of simultaneous equations with heteroskedasticity," Econometric Reviews, Taylor & Francis Journals, vol. 38(4), pages 359-385, April.
    18. 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.
    19. Xiaodong Liu & Lung-Fei Lee, 2013. "Two-Stage Least Squares Estimation of Spatial Autoregressive Models with Endogenous Regressors and Many Instruments," Econometric Reviews, Taylor & Francis Journals, vol. 32(5-6), pages 734-753, August.
    20. Lee, Lung-fei, 2007. "GMM and 2SLS estimation of mixed regressive, spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 137(2), pages 489-514, April.
    21. Liu, Xiaodong & Saraiva, Paulo, 2015. "GMM estimation of SAR models with endogenous regressors," Regional Science and Urban Economics, Elsevier, vol. 55(C), pages 68-79.
    22. Yang, Kai & Lee, Lung-fei, 2017. "Identification and QML estimation of multivariate and simultaneous equations spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 196(1), pages 196-214.
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    Cited by:

    1. Peter H. Egger & Ingmar R. Prucha, 2023. "Refined GMM estimators for simultaneous equations models with network interactions," Empirical Economics, Springer, vol. 64(6), pages 2535-2542, June.
    2. Marius C. O. Amba, 2021. "Simultaneous Equations with Three Way Error Components," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(3), pages 583-596, September.

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    More about this item

    Keywords

    Social networks; Quadratic moment conditions; Efficiency; Many-instrument bias;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation

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