Guido M. Kuersteiner, Ingmar R. Prucha, Ying Zeng
arXiv 10 May 2021 · Econometrics · publishedJournal of Econometrics (2023) · 3 citations (OpenAlex)
arXiv:2105.04330 · PDF · DOI · OpenAlex · Extracted main text
We study linear peer effects models where peers interact in groups, individual's outcomes are linear in the group mean outcome and characteristics, and group effects are random. Our specification is motivated by the moment conditions imposed in Graham 2008. We show that these moment conditions can be cast in terms of a linear random group effects model and lead to a class of GMM estimators that are generally identified as long as there is sufficient variation in group size. We also show that our class of GMM estimators contains a Quasi Maximum Likelihood estimator (QMLE) for the random group effects model, as well as the Wald estimator of Graham 2008 and the within estimator of Lee 2007 as special cases. Our identification results extend insights in Graham 2008 that show how assumptions about random group effects as well as variation in group size can be used to overcome the reflection problem in identifying peer effects. Our QMLE and GMM estimators accommodate additional covariates and are valid in situations with a large but finite number of different group sizes or types. Because our estimators are general moment based procedures, using instruments other than binary group indicators in estimation is straight forward. Our QMLE estimator accommodates group level covariates in the spirit of Mundlak and Chamberlain and offers an alternative to fixed effects specifications. Monte-Carlo simulations show that the bias of the QMLE estimator decreases with the number of groups and the variation in group size, and increases with group size. We also prove the consistency and asymptotic normality of the estimator under reasonable assumptions.
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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 | Lee, L.F (2007) Identification and estimation of econometric models with group interactions, contextual factors and fixed effects | 1.000 | 19 | 5 | 100% |
| 2 | Kelejian, H.H., Prucha, I.R (2002) 2SLS and OLS in a spatial autoregressive model with equal spatial weights self | 0.928 | 4 | 3 | 100% |
| 3 | Graham, B.S (2008) Identifying Social Interactions Through Conditional Variance Restrictions | 0.879 | 28 | 5 | 68% |
| 4 | Bramoullé, Y., Djebbari, H., Fortin, B (2009) Identification of peer effects through social networks | 0.874 | 6 | 3 | 67% |
| 5 | Manski, C.F (1993) Identification of Endogenous Social Effects: The Reflection Problem | 0.843 | 5 | 3 | 60% |
| 6 | Kelejian, H.H., Prucha, I.R., Yuzefovich, Y (2006) Estimation Problems in Models with Spatial Weighting Matrices Which Have Blocks of Equal Elements* self | 0.811 | 5 | 2 | 80% |
| 7 | Angrist, J.D (2014) The perils of peer effects | 0.737 | 4 | 3 | 50% |
| 8 | Kelejian, H.H., Prucha, I.R (1998) A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Distur… self | 0.737 | 3 | 2 | 100% |
| 9 | Kuersteiner, G.M., Prucha, I.R (2020) Dynamic Spatial Panel Models: Networks, Common Shocks, and Sequential Exogeneity self | 0.737 | 3 | 2 | 100% |
| 10 | Kelejian, H.H., Prucha, I.R (1999) A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model self | 0.644 | 3 | 2 | 67% |
Showing the top 10 of 52 scored citations.