  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.
