arXiv 22 Nov 2020 · Econometrics
arXiv:2011.11084 · PDF · DOI · OpenAlex · Extracted main text
We study identifiability of the parameters in autoregressions defined on a network. Most identification conditions that are available for these models either rely on the network being observed repeatedly, are only sufficient, or require strong distributional assumptions. This paper derives conditions that apply even when the individuals composing the network are observed only once, are necessary and sufficient for identification, and require weak distributional assumptions. We find that the model parameters are generically, in the measure theoretic sense, identified even without repeated observations, and analyze the combinations of the interaction matrix and the regressor matrix causing identification failures. This is done both in the original model and after certain transformations in the sample space, the latter case being relevant, for example, in some fixed effects specifications.
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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., Fortin, B (2009) Identification of peer effects through social networks | 1.000 | 9 | 4 | 100% |
| 2 | Lee, L.-F (2004) Asymptotic distributions of quasi-maximum likelihood estimators for spatial autoregressive models | 1.000 | 7 | 4 | 100% |
| 3 | Preinerstorfer, D., Pötscher, B. M (2017) On the power of invariant tests for hypotheses on a covariance matrix | 0.843 | 4 | 3 | 75% |
| 4 | Kelejian, H. H., Prucha, I. R (1998) A generalized spatial two-stage least squares procedure for estimating a spatial autoregressive model with autoregressive distur… | 0.737 | 3 | 2 | 100% |
| 5 | Newey, W. K., McFadden, D (1994) Large sample estimation and hypothesis testing | 0.737 | 3 | 2 | 100% |
| 6 | Lee, L.-F., Liu, X., Lin, X (2010) Specification and estimation of social interaction models with network structures | 0.693 | 8 | 2 | 50% |
| 7 | Manski, C. F (1993) Identification of endogenous social effects: The reflection problem | 0.644 | 2 | 2 | 100% |
| 8 | Lee, L.-F., Yu, J (2016) Identification of spatial Durbin panel models | 0.585 | 3 | 1 | 100% |
| 9 | Lehmann, E. L., Romano, J. P (2005) Testing Statistical Hypotheses, 3rd Edition | 0.585 | 3 | 1 | 100% |
| 10 | Yu, D., Bai, P., Ding, C (2015) Adjusted quasi-maximum likelihood estimator for mixed regressive, spatial autoregressive model and its small sample bias | 0.511 | 2 | 2 | 50% |
Showing the top 10 of 41 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Peer effect analysis with latent processes | 0.405 | 1 | 1 |
| 2 | Estimating peer effects in noisy, low-rank networks via network smoothing | 0.405 | 1 | 1 |