arXiv 9 Oct 2020 · Econometrics · 4 citations (OpenAlex)
arXiv:2010.04703 · PDF · DOI · OpenAlex · Extracted main text
Consider a bipartite network where $N$ consumers choose to buy or not to buy $M$ different products. This paper considers the properties of the logistic regression of the $N\times M$ array of i-buys-j purchase decisions, $\left[Y_{ij}\right]_{1\leq i\leq N,1\leq j\leq M}$, onto known functions of consumer and product attributes under asymptotic sequences where (i) both $N$ and $M$ grow large and (ii) the average number of products purchased per consumer is finite in the limit. This latter assumption implies that the network of purchases is sparse: only a (very) small fraction of all possible purchases are actually made (concordant with many real-world settings). Under sparse network asymptotics, the first and last terms in an extended Hoeffding-type variance decomposition of the score of the logit composite log-likelihood are of equal order. In contrast, under dense network asymptotics, the last term is asymptotically negligible. Asymptotic normality of the logistic regression coefficients is shown using a martingale central limit theorem (CLT) for triangular arrays. Unlike in the dense case, the normality result derived here also holds under degeneracy of the network graphon. Relatedly, when there happens to be no dyadic dependence in the dataset in hand, it specializes to recently derived results on the behavior of logistic regression with rare events and iid data. Sparse network asymptotics may lead to better inference in practice since they suggest variance estimators which (i) incorporate additional sources of sampling variation and (ii) are valid under varying degrees of dyadic dependence.
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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 | Wang, H (2020) Pmlr | 1.000 | 8 | 3 | 100% |
| 2 | Graham, B. S (2020) Handbook of Econometrics, volume 7, chapter Network data self | 1.000 | 5 | 4 | 100% |
| 3 | Menzel, K (2017) Bootstrap with clustering in two or more dimensions | 1.000 | 5 | 3 | 100% |
| 4 | Bickel, P. J., Chen, A., and Levina, E (2011) The method of moments and degree distributions for network models | 0.928 | 4 | 3 | 100% |
| 5 | Davezies, L., d'Haultfoeuille, X., and Guyonvarch, Y (2020) Empirical process results for exchangeable arrayes | 0.843 | 3 | 3 | 100% |
| 6 | Graham, B. S (2020) The Econometrics of Social and Economic Networks, chapter Dyadic regression, pages 25 – 41 self | 0.843 | 3 | 3 | 100% |
| 7 | King, G. and Zeng, L (2001) Logistic regression in rare events data | 0.811 | 4 | 2 | 100% |
| 8 | Aldous, D. J (1981) Representations for partially exchangeable arrays of random variables | 0.737 | 3 | 2 | 100% |
| 9 | Aronow, P. M., Samii, C., and Assenova, V. A (2017) Cluster-robust variance estimation for dyadic data | 0.737 | 3 | 2 | 100% |
| 10 | Cameron, A. C. and Miller, D. L (2014) Robust inference for dyadic data | 0.737 | 3 | 2 | 100% |
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