# Nonlinear Factor Models for Network and Panel Data

arXiv: https://arxiv.org/abs/1412.5647 · HTML: https://mlsanity.com/paper/1412.5647/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2014.

## Built on by (most cited first, 1 of 1)

- [Learning Nonlinear Factor Models with Unknown Monotone Links from Incomplete and Noisy Data](https://mlsanity.com/paper/2605.26271/) (2026)

## Abstract (arXiv)

Factor structures or interactive effects are convenient devices to incorporate latent variables in panel data models. We consider fixed effect estimation of nonlinear panel single-index models with factor structures in the unobservables, which include logit, probit, ordered probit and Poisson specifications. We establish that fixed effect estimators of model parameters and average partial effects have normal distributions when the two dimensions of the panel grow large, but might suffer of incidental parameter bias. We show how models with factor structures can also be applied to capture important features of network data such as reciprocity, degree heterogeneity, homophily in latent variables and clustering. We illustrate this applicability with an empirical example to the estimation of a gravity equation of international trade between countries using a Poisson model with multiple factors.