Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/211111
Authors: 
Chen, Mingli
Fernández-Val, Iván
Weidner, Martin
Year of Publication: 
2019
Series/Report no.: 
cemmap working paper No. CWP18/19
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.
Subjects: 
Panel data
network data
interactive fixed effects
factor models
bias correction
incidental parameter problem
gravity equation
JEL: 
C13
C23
Document Type: 
Working Paper

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