arXiv 8 Aug 2024 · Econometrics
arXiv:2408.04552 · PDF · DOI · OpenAlex · Extracted main text
Dyadic network formation models have wide applicability in economic research, yet are difficult to estimate in the presence of individual specific effects and in the absence of distributional assumptions regarding the model noise component. The availability of (continuously distributed) individual or link characteristics generally facilitates estimation. Yet, while data on social networks has recently become more abundant, the characteristics of the entities involved in the link may not be measured. Adapting the procedure of \citet{KS}, I propose to use network data alone in a semiparametric estimation of the individual fixed effect coefficients, which carry the interpretation of the individual relative popularity. This entails the possibility to anticipate how a new-coming individual will connect in a pre-existing group. The estimator, needed for its fast convergence, fails to implement the monotonicity assumption regarding the model noise component, thereby potentially reversing the order if the fixed effect coefficients. This and other numerical issues can be conveniently tackled by my novel, data-driven way of normalising the fixed effects, which proves to outperform a conventional standardisation in many cases. I demonstrate that the normalised coefficients converge both at the same rate and to the same limiting distribution as if the true error distribution was known. The cost of semiparametric estimation is thus purely computational, while the potential benefits are large whenever the errors have a strongly convex or strongly concave distribution.
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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 | Klein, R. W. and R. H. Spady (1993) An efficient semiparametric estimator for binary response models | 0.928 | 5 | 5 | 80% |
| 2 | Graham, B. S (2017) An econometric model of network formation with degree heterogeneity | 0.585 | 3 | 1 | 100% |
| 3 | Candelaria, L. E (2017) A semiparametric network formation model with multiple linear fixed effects | 0.511 | 2 | 1 | 100% |
| 4 | Gao, W. Y (2020) Nonparametric identification in index models of link formation | 0.405 | 1 | 1 | 100% |
| 5 | Jochmans, K (2018) Semiparametric analysis of network formation | 0.405 | 1 | 1 | 100% |
| 6 | Ridder, G. and S. Sheng (2015) Estimation of large network formation games | 0.405 | 1 | 1 | 100% |
| 7 | Ridder, G. and S. Sheng (2020) Two-step estimation of a strategic network formation model with clustering | 0.405 | 1 | 1 | 100% |
| 8 | Dzemski, A (2019) An empirical model of dyadic link formation in a network with unobserved heterogeneity | 0.405 | 1 | 1 | 100% |
| 9 | Mele, A (2017) A structural model of dense network formation | 0.405 | 1 | 1 | 100% |
| 10 | Sheng, S (2020) A structural econometric analysis of network formation games through subnetworks | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 13 scored citations.