Nonlinear Factor Models for Network and Panel Data

Mingli Chen, Iv\'an Fern\'andez-Val and Martin Weidner
Authors registered in the RePEc Author Service: Ivan Fernandez-Val

Papers from arXiv.org

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 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.

Date: 2014-12, Revised 2019-10
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Citations: View citations in EconPapers (10)

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Related works:
Journal Article: Nonlinear factor models for network and panel data (2021)
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:arx:papers:1412.5647

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