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Nonlinear Factor Models for Network and Panel Data

Chen, M; Fernández-Val, I; Weidner, M; (2021) Nonlinear Factor Models for Network and Panel Data. Journal of Econometrics , 220 (2) pp. 296-324. 10.1016/j.jeconom.2020.04.004. Green open access

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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 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.

Type: Article
Title: Nonlinear Factor Models for Network and Panel Data
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.jeconom.2020.04.004
Publisher version: https://doi.org/10.1016/j.jeconom.2020.04.004
Language: English
Additional information: Copyright © 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
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Economics
URI: https://discovery.ucl.ac.uk/id/eprint/10087709
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