Nonlinear factor models for network and panel data

Mingli Chen, Ivan Fernandez-Val () and Martin Weidner

Journal of Econometrics, 2021, vol. 220, issue 2, 296-324

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 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.

Keywords: Panel data; Network data; Interactive fixed effects; Factor models; Bias correction; Incidental parameter problem; Gravity equation (search for similar items in EconPapers)
JEL-codes: C13 C23 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (25)

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Related works:
Working Paper: Nonlinear Factor Models for Network and Panel Data (2019)
Working Paper: Nonlinear factor models for network and panel data (2019)
Working Paper: Nonlinear factor models for network and panel data (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:220:y:2021:i:2:p:296-324

DOI: 10.1016/j.jeconom.2020.04.004

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