Nonlinear factor models for network and panel data

Mingli Chen

doi:10.1016/j.jeconom.2020.04.004

Nonlinear factor models for network and panel data

2021-02-01

32

OA

AI

DOI：10.1016/j.jeconom.2020.04.004

Original

Save

View PDF

Abstract

En 中文

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. (c) 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/).

Keywords:

Panel data

Network data

Interactive fixed effects

Factor models

Bias correction

Incidental parameter problem

Gravity equation

Nonlinear factor models for network and panel data

Abstract

AI Summary

Journal

Organization