arXiv 2 Aug 2019 · Econometrics
arXiv:1908.00663 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes a new method to identify leaders and followers in a network. Prior works use spatial autoregression models (SARs) which implicitly assume that each individual in the network has the same peer effects on others. Mechanically, they conclude the key player in the network to be the one with the highest centrality. However, when some individuals are more influential than others, centrality may fail to be a good measure. I develop a model that allows for individual-specific endogenous effects and propose a two-stage LASSO procedure to identify influential individuals in a network. Under an assumption of sparsity: only a subset of individuals (which can increase with sample size n) is influential, I show that my 2SLSS estimator for individual-specific endogenous effects is consistent and achieves asymptotic normality. I also develop robust inference including uniformly valid confidence intervals. These results also carry through to scenarios where the influential individuals are not sparse. I extend the analysis to allow for multiple types of connections (multiple networks), and I show how to use the sparse group LASSO to detect which of the multiple connection types is more influential. Simulation evidence shows that my estimator has good finite sample performance. I further apply my method to the data in Banerjee et al. (2013) and my proposed procedure is able to identify leaders and effective networks.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bramoullé, Y., Djebbari, H., and Fortin, B (2009) Identification of peer effects through social networks | 1.000 | 8 | 3 | 100% |
| 2 | van de Geer, S., Buhlmann, P., Ritov, Y., and Dezeure, R (2014) On asymptotically optimal confidence regions and tests for high-dimensional models | 1.000 | 6 | 5 | 100% |
| 3 | Zhu, Y (2018) Sparse linear models and l1−regularized 2sls with high-dimensional endogenous regressors and instruments | 1.000 | 6 | 3 | 100% |
| 4 | Manski, C (1993) Identification of endogenous social effects: The reflection problem | 1.000 | 5 | 3 | 100% |
| 5 | Banerjee, A., Chandrasekhar, A., Duflo, E., and Jackson, M (2013) The diffusion of microfinance | 0.928 | 4 | 3 | 100% |
| 6 | de Paula, A., Rasul, I., and Souza, P. C (2015) Recovering social networks from panel data: identification, simulations and an application | 0.928 | 4 | 3 | 100% |
| 7 | Kelejian, H. H. and Prucha, I. R (1998) A generalized spatial two-stage least squares procedure for estimating a spatial autoregressive model with autoregressive distur… | 0.874 | 5 | 2 | 100% |
| 8 | Anselin, L (1988) Spatial Econometrics: Methods and Models | 0.811 | 4 | 2 | 100% |
| 9 | Lee, L (2002) Consistency and efficiency of least squares estimation for mixed regressive, spatial | 0.737 | 3 | 2 | 100% |
| 10 | Jin, F. and Lee, L.-F (2018) Lasso maximum likelihood estimation of parametric models with singular information matrices | 0.737 | 3 | 2 | 100% |
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