Nonlinear factor models for network and panel data
OA Version
Published version
Citation
M. Chen, I. Fernandez-Val, M. Weidner. 2021. "Nonlinear Factor Models for Network and Panel Data." Journal of Econometrics, Volume 220, pp. 296 - 324.
https://doi.org/10.1016/j.jeconom.2020.04.004.
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 also show how models with factor structures can 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.
Description
License
© 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)