arXiv 5 Aug 2026 · Econometrics
arXiv:2608.04983 · PDF · Extracted main text
I develop an estimation and inference framework for distribution regression in dyadic network settings with two-way fixed effects that vary across thresholds of the outcome. I show that identification of the structural parameters is achieved through binarization of the outcome at each threshold, and estimate the model by conditional maximum likelihood, which "differences out" the fixed effects and circumvents the incidental parameter problem. The estimator remains asymptotically unbiased under sparsity, whether from the network structure or binarization at extreme thresholds. The second novelty is to establish the joint asymptotic distribution of the estimators across multiple thresholds with different convergence rates, and to develop simultaneous confidence bands and tests for equality of coefficients across thresholds. Monte Carlo simulations confirm small bias, valid inference, and correct simultaneous coverage under sparsity. An application to bilateral trade finds that coefficients vary substantially across the distribution, with equality rejected for key trade barriers.
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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 | Charbonneau, K. B (2017) Multiple fixed effects in binary response panel data models | 0.961 | 9 | 5 | 89% |
| 2 | Chernozhukov, V., I. Fernandez-Val, and M. Weidner (2024) Network and panel quantile effects via distribution regression | 0.953 | 15 | 7 | 87% |
| 3 | Helpman, E., M. Melitz, and Y. Rubinstein (2008) Estimating trade flows: Trading partners and trading volumes | 0.950 | 7 | 4 | 86% |
| 4 | Graham, B. S (2017) An econometric model of network formation with degree heterogeneity | 0.888 | 10 | 5 | 70% |
| 5 | Montiel Olea, J. L. and M. Plagborg-Mller (2019) Simultaneous confidence bands: Theory, implementation, and an application to SVARs | 0.874 | 8 | 2 | 100% |
| 6 | Jochmans, K (2018) Semiparametric analysis of network formation | 0.857 | 27 | 9 | 63% |
| 7 | Chernozhukov, V., I. Fernández-Val, and B. Melly (2013) b): Inference on counterfactual distributions | 0.843 | 3 | 3 | 100% |
| 8 | Muris, C., C. Pakel, and Q. Zhang (2025) Dyadic data with ordered outcome variables | 0.737 | 3 | 3 | 67% |
| 9 | Baltagi, B. H. and P. Egger (2016) Estimation of structural gravity quantile regression models | 0.737 | 3 | 2 | 100% |
| 10 | Bergstrand, J. H., M. W. Clance, and J. S. Silva (2025) The tails of gravity: Using expectiles to quantify the trade-margins effects of economic integration agreements | 0.737 | 3 | 2 | 100% |
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