arXiv 15 Jun 2026 · Econometrics
arXiv:2606.16230 · PDF · DOI · OpenAlex · Extracted main text
This paper develops identification and estimation methods for a semiparametric dynamic logit model in which a binary outcome depends on observed covariates, the lagged outcome, and an unknown function of a latent social characteristic that also governs the formation of social ties. The unobserved characteristic is allowed to vary across agents and over time, and the network formation process is left completely unspecified. Identification combines three elements: conditional likelihood arguments that exploit the logistic structure, network-type matching that eliminates the unknown social influence function by comparing agents whose observed linking behavior reveals identical latent characteristics, and local temporal smoothing that handles the interaction between dynamics and time-varying unobserved heterogeneity. A kernel-weighted conditional maximum likelihood estimator is proposed, and its consistency and asymptotic normality are established at the $\sqrt{n}$ rate. Monte Carlo simulations show that the estimator substantially reduces the bias present in naive and control-function approaches across a range of network formation models and achieves close to nominal coverage at moderate sample sizes. The method is applied to longitudinal data on adolescent smoking and friendship networks from the Glasgow Teenage Friends and Lifestyle Study. An extension to ordered outcomes is developed using composite conditional maximum likelihood.
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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 | Auerbach, E (2022) Identification and estimation of a partially linear regression model using network data | 1.000 | 5 | 4 | 100% |
| 2 | Muris, C., Raposo, P., and Vandoros S (2025) A dynamic ordered logit model with fixed effects | 1.000 | 5 | 3 | 100% |
| 3 | Gueyap Kounga, B. R (2026) Identification and estimation of a semiparametric logit model using network data | 0.971 | 12 | 6 | 92% |
| 4 | Honoré, B. E. and Kyriazidou, E (2000) Panel data discrete choice models with lagged dependent variables | 0.950 | 7 | 4 | 86% |
| 5 | Chamberlain, G (1980) Analysis of covariance with qualitative data | 0.843 | 3 | 3 | 100% |
| 6 | Fletcher, J. M (2010) Social interactions and smoking: Evidence using multiple student cohorts, instrumental variables, and school fixed effects | 0.843 | 3 | 3 | 100% |
| 7 | Newey, W. K. and McFadden, D (1994) Large sample estimation and hypothesis testing | 0.737 | 5 | 2 | 60% |
| 8 | Conley, T. G. and Udry, C. R (2010) Learning about a new technology: Pineapple in Ghana | 0.737 | 3 | 2 | 100% |
| 9 | Calvó-Armengol, A., Patacchini, E., and Zenou, Y (2009) Peer effects and social networks in education | 0.644 | 2 | 2 | 100% |
| 10 | Nakajima, R (2007) Measuring peer effects on youth smoking behaviour | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 38 scored citations.