Semiparametric single-index panel data models with cross-sectional dependence

Dong, C; Gao, J; Peng, B

Semiparametric single-index panel data models with cross-sectional dependence

Dong, C Gao, J Peng, B

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Publication Type:: Journal Article
Citation:: Journal of Econometrics, 2015, 188 (1), pp. 301 - 312
Issue Date:: 2015-09-01

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Field	Value	Language
dc.contributor.author	Dong, C	en_US
dc.contributor.author	Gao, J	en_US
dc.contributor.author	Peng, B https://orcid.org/0000-0003-4231-4713	en_US
dc.date.available	2020-05-25T19:04:57Z
dc.date.issued	2015-09-01	en_US
dc.identifier.citation	Journal of Econometrics, 2015, 188 (1), pp. 301 - 312	en_US
dc.identifier.issn	0304-4076	en_US
dc.identifier.uri	http://hdl.handle.net/10453/41165
dc.description.abstract	© 2015 Elsevier B.V. Abstract In this paper, we consider a semiparametric single-index panel data model with cross-sectional dependence and stationarity. Meanwhile, we allow fixed effects to be correlated with the regressors to capture unobservable heterogeneity. Under a general spatial error dependence structure, we then establish some consistent closed-form estimates for both the unknown parameters and the link function for the case where both cross-sectional dimension (N) and temporal dimension (T) go to infinity. Rates of convergence and asymptotic normality are established for the proposed estimates. Our experience suggests that the proposed estimation method is simple and thus attractive for finite-sample studies and empirical implementations. Moreover, both the finite-sample performance and the empirical applications show that the proposed estimation method works well when the cross-sectional dependence exists in the data set.	en_US
dc.relation.ispartof	Journal of Econometrics	en_US
dc.relation.isbasedon	10.1016/j.jeconom.2015.06.001	en_US
dc.subject.classification	Econometrics	en_US
dc.title	Semiparametric single-index panel data models with cross-sectional dependence	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	188	en_US
utslib.for	1403 Econometrics	en_US
utslib.for	0104 Statistics	en_US
utslib.for	1402 Applied Economics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	open_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	188	en_US

Abstract:

© 2015 Elsevier B.V. Abstract In this paper, we consider a semiparametric single-index panel data model with cross-sectional dependence and stationarity. Meanwhile, we allow fixed effects to be correlated with the regressors to capture unobservable heterogeneity. Under a general spatial error dependence structure, we then establish some consistent closed-form estimates for both the unknown parameters and the link function for the case where both cross-sectional dimension (N) and temporal dimension (T) go to infinity. Rates of convergence and asymptotic normality are established for the proposed estimates. Our experience suggests that the proposed estimation method is simple and thus attractive for finite-sample studies and empirical implementations. Moreover, both the finite-sample performance and the empirical applications show that the proposed estimation method works well when the cross-sectional dependence exists in the data set.

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http://hdl.handle.net/10453/41165