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Updated A.Y. 2022-2023

“Man is by nature a social animal... He who lives without society is either a beast or God" (Aristotele- Politics, Book I, Part II).

In economics, the importance of (social) interactions outside the market is now well recognized. Individuals share information, learn from each other’s, and influence each other in many contexts.

The course introduces the game-theoretical foundation of the social interactions model and focuses on how to identify and structurally estimate the parameters of these models. Social interactions models are a particular case of simultaneous equations models, i.e., statistical models where the dependent variables are jointly determined by other dependent variables together with independent ones. Many economic models are simultaneous in nature as a consequence of the underlying equilibrium mechanism. A leading example is the estimation of the utility parameters of the equilibrium equation system in the economy under social interactions.  


  1. Description of Networks: Centrality Measures.
  • Bonacich, P. (1987), Power and centrality: A family of measures, American Journal of Sociology, 92(5): 1170-1182.
  • Katz L., `A new index derived from sociometric data analysis, Psychometrika, 18: 39-43.


  1. Social Interactions Models: Micro-foundation and Identification
  • Bramoullé, Y., Djebbari, H. and Fortin, B., 2020. Peer effects in networks: A survey. Annual Review of Economics12, pp.603-629.
  • De Paula, A., 2017, January. Econometrics of network models. In Advances in Economics and Econometrics: Theory and Applications: Eleventh World Congress(Vol. 1, pp. 268-323). Cambridge: Cambridge University Press.
  • Blume, L.E., Brock, W.A., Durlauf, S.N. and Jayaraman, R., 2015. Linear social interactions models. Journal of Political Economy123(2), pp.444-496.
  • Calvó-Arkmengol, A., Patacchini, E. and Zenou, Y., 2009. Peer effects and social networks in education. The review of economic studies76(4), pp.1239-1267.
  • Manski, C.F., 1993. Identification of endogenous social effects: The reflection problem. The review of economic studies60(3), pp.531-542.
  • Lee, L.F., 2007. Identification and estimation of econometric models with group interactions, contextual factors and fixed effects. Journal of Econometrics140(2), pp.333-374.
  • Bramoullé, Y., Djebbari, H. and Fortin, B., 2009. Identification of peer effects through social networks. Journal of econometrics150(1), pp.41-55.
  • Lee, L.F., Liu, X. and Lin, X., 2010. `` Specification and estimation of social interaction models with network structures". The Econometrics Journal, 13(2), pp.145-176.


  1. Social Interactions Models: Estimation and Inference
  • Lee, L.F., Asymptotic distributions of quasi-maximum likelihood estimators for spatial autoregressive
    models. Econometrica, 72(6):1899-1925, 2004
  • Kelejian, H. H., Prucha, I. R. A generalized spatial two stage least squares procedure for estimating
    a spatial autoregressive model with autoregressive disturbances. Journal of Real Estate Finance
    and Economics, 17(1):99-121, 1998.
  • Lee, L.F., 2007. Identi cation and estimation of econometric models with group interactions,
    contextual factors and  xed e ects. Journal of Econometrics, 140(2), pp.333-374.
  • Lee, L.F., Liu, X. and Lin, X., 2010. Speci cation and estimation of social interaction models
    with network structures. The Econometrics Journal, 13(2), pp.145-176.
  • Liu, X. and Lee, L.F., 2010. GMM estimation of social interaction models with centrality. Journal
    of Econometrics, 159(1), pp.99-115
  1. Model Extensions: Endogeneity, Quasi-random Variations, Heterogeneity and Treatment Effects
  • Angrist, J.D., 2014. The perils of peer effects. Labour Economics30, pp.98-108.
  • Arduini, T., Patacchini, E. and Rainone, E., 2020. Identification and estimation of network models with heterogeneous interactions. In The Econometrics of Networks. Emerald Publishing Limited.
  • Arduini, T., Patacchini, E. and Rainone, E., 2020. Treatment effects with heterogeneous externalities. Journal of Business & Economic Statistics38(4), pp.826-838.
  • Bifulco, Robert, Jason M. Fletcher, and Stephen L. Ross. The effect of classmate characteristics on post-secondary outcomes: Evidence from the Add Health. American Economic Journal: Economic Policy 3.1 (2011): 25-53.
  • Johnsson, I. and Moon, H.R., 2021. Estimation of peer effects in endogenous social networks: Control function approach. Review of Economics and Statistics, 103(2), pp.328-345.

The complete list of references is given in the slides.