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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 specifcations. 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.
Authors
Research Associate University College London and University of Oxford
Martin is an IFS Research Associate, a Fellow of the Nuffield College and a Professor in the Department of Economics at the University of Oxford.
Ivan Fernandez-Val
Mingli Chen
Working Paper details
- DOI
- 10.1920/wp.cem.2019.1819
- Publisher
- The IFS
Suggested citation
M, Chen and I, Fernandez-Val and M, Weidner. (2019). Nonlinear factor models for network and panel data. London: The IFS. Available at: https://ifs.org.uk/publications/nonlinear-factor-models-network-and-panel-data (accessed: 24 April 2024).
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