arXiv 30 Sep 2025 · Econometrics
arXiv:2509.26420 · PDF · Extracted main text
We study estimation and inference for triadic link formation with dyad-level fixed effects in a nonlinear binary choice logit framework. Dyad-level effects provide a richer and more realistic representation of heterogeneity across pairs of dimensions (e.g. importer-exporter, importer-product, exporter-product), yet their sheer number creates a severe incidental parameter problem. We propose a novel “hexad logit” estimator and establish its consistency and asymptotic normality. Identification is achieved through a conditional likelihood approach that eliminates the fixed effects by conditioning on sufficient statistics, in the form of hexads -- wirings that involve two nodes from each part of the network. Our central finding is that dyad-level heterogeneity fundamentally changes how information accumulates. Unlike under node-level heterogeneity, where informative wirings automatically grow with link formation, under dyad-level heterogeneity the network may generate infinitely many links yet asymptotically zero informative wirings. We derive explicit sparsity thresholds that determine when consistency holds and when asymptotic normality is attainable. These results have important practical implications, as they reveal that there is a limit to how granular or disaggregate a dataset one can employ under dyad-level heterogeneity.
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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 | Jochmans, K (2018) Semiparametric analysis of network formation | 0.920 | 9 | 5 | 78% |
| 2 | Charbonneau, K. B (2017) Multiple fixed effects in binary response panel data models | 0.894 | 7 | 4 | 71% |
| 3 | Graham, B (2017) An econometric model of network formation with degree heterogeneity | 0.825 | 16 | 6 | 56% |
| 4 | Arellano, M. and S. Bonhomme (2011) Nonlinear panel data analysis | 0.644 | 2 | 2 | 100% |
| 5 | Serfling, R. J (1980) Approximation Theorems of Mathematical Statistics | 0.511 | 2 | 1 | 100% |
| 6 | Andersen, E. B (1970) Properties of conditional maximum-likelihood estimators | 0.405 | 1 | 1 | 100% |
| 7 | Boehm, J., S. Dhingra, and J. Morrow (2022) The comparative advantage of firms | 0.405 | 1 | 1 | 100% |
| 8 | Bernard, A. B., A. Moxnes, and K. H. Ulltveit-Moe (2018) Two-sided heterogeneity and trade | 0.405 | 1 | 1 | 100% |
| 9 | Balazsi, L., L. Matyas, and T. Wansbeek (2017) Fixed effects models | 0.405 | 1 | 1 | 100% |
| 10 | Bernard, A. B., A. Moxnes, and Y. U. Saito (2019) Production networks, geography, and firm performance | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 29 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Binary choice logit models with general fixed effects for panel and network data | 0.585 | 3 | 1 |
| 2 | Statistical inference in large multi-way networks | 0.511 | 2 | 1 |
| 3 | (Debiased) Inference for Fixed Effects Estimators with Three-Dimensional Panel and Network Data | 0.405 | 1 | 1 |