Nonlinear factor models for network and panel data

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 specif...

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Published in:	Journal of econometrics Vol. 220; no. 2; pp. 296 - 324
Main Authors:	Chen, Mingli, Fernández-Val, Iván, Weidner, Martin
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 01.02.2021 Elsevier Sequoia S.A
Subjects:	Bias Bias correction Clustering econometric models economic analysis economic theory equations Estimating techniques Estimation Factor models Factor structures Gravity Gravity equation Incidental parameter problem Interactive fixed effects International trade Longitudinal studies Network data Nonlinear systems Panel data Poisson distribution Reciprocity Variables Bias correction Panel data C13 C23 Interactive fixed effects Factor models Gravity equation Incidental parameter problem Network data
ISSN:	0304-4076, 1872-6895
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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.
AbstractList	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.
Author	Fernández-Val, Iván Weidner, Martin Chen, Mingli
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Snippet	Factor structures or interactive effects are convenient devices to incorporate latent variables in panel data models. We consider fixed effect estimation of...
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SubjectTerms	Bias Bias correction Clustering econometric models economic analysis economic theory equations Estimating techniques Estimation Factor models Factor structures Gravity Gravity equation Incidental parameter problem Interactive fixed effects International trade Longitudinal studies Network data Nonlinear systems Panel data Poisson distribution Reciprocity Variables
Title	Nonlinear factor models for network and panel data
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