Nonlinear factor models for network and panel data
Files
First author draft
Date
DOI
Authors
Fernández-Val, Iván
Chen, Mingli
Weidner, Martin
Version
OA Version
First author draft
Citation
Ivan Fernandez-Val, Mingli Chen, Martin Weidner. 2019. "Nonlinear Factor Models for Network and Panel Data." arXiv:1412.5647
Abstract
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 from 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.