Nonlinear factor models for network and panel data

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 specif...

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Bibliographic Details
Published in:Journal of econometrics 2021-02, Vol.220 (2), p.296-324
Main Authors: Chen, Mingli, Fernández-Val, Iván, Weidner, Martin
Format: Article
Language:English
Subjects:
Bias
Bias correction
Clustering
Estimating techniques
Factor models
Factor structures
Gravity
Gravity equation
Incidental parameter problem
Interactive fixed effects
International trade
Longitudinal studies
Network data
Nonlinear systems
Panel data
Poisson distribution
Reciprocity
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Summary: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.
ISSN:0304-4076
1872-6895
DOI:10.1016/j.jeconom.2020.04.004