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Nonlinear factor models for network and panel data

Authors: Mingli Chen , Ivan Fernandez-Val and Martin Weidner
Date: 03 July 2018
Type: cemmap Working Paper, CWP38/18
DOI: 10.1920/wp.cem.2018.3818


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 specifi cations. We establish that fi xed 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.

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Mingli Chen, Ivan Fernandez-Val and Martin Weidner April 2019, Nonlinear factor models for network and panel data, cemmap Working Paper, CWP18/19, The IFS

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