Heteroscedastic transformation models with covariate dependent censoring

Khan, S; Shin, Y; Tamer, E

Heteroscedastic transformation models with covariate dependent censoring

Khan, S Shin, Y

Tamer, E

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Publication Type:: Journal Article
Citation:: Journal of Business and Economic Statistics, 2011, 29 (1), pp. 40 - 48
Issue Date:: 2011-01-01

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Field	Value	Language
dc.contributor.author	Khan, S	en_US
dc.contributor.author	Shin, Y https://orcid.org/0000-0002-2711-6906	en_US
dc.contributor.author	Tamer, E	en_US
dc.date.issued	2011-01-01	en_US
dc.identifier.citation	Journal of Business and Economic Statistics, 2011, 29 (1), pp. 40 - 48	en_US
dc.identifier.issn	0735-0015	en_US
dc.identifier.uri	http://hdl.handle.net/10453/112683
dc.description.abstract	In this article we propose an inferential procedure for transformation models with conditional heteroscedasticity in the error terms. The proposed method is robust to covariate dependent censoring of arbitrary form.We provide sufficient conditions for point identification.We then propose an estimator and show that it is √n-consistent and asymptotically normal. We conduct a simulation study that reveals adequate finite sample performance. We also use the estimator in an empirical illustration of export duration, where we find advantages of the proposed method over existing ones. © 2011 American Statistical Association.	en_US
dc.relation.ispartof	Journal of Business and Economic Statistics	en_US
dc.relation.isbasedon	10.1198/jbes.2009.07227	en_US
dc.subject.classification	Econometrics	en_US
dc.title	Heteroscedastic transformation models with covariate dependent censoring	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	29	en_US
utslib.for	1403 Econometrics	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	14 Economics	en_US
utslib.for	15 Commerce, Management, Tourism and Services	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 Business
utslib.copyright.status	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	29	en_US

Abstract:

In this article we propose an inferential procedure for transformation models with conditional heteroscedasticity in the error terms. The proposed method is robust to covariate dependent censoring of arbitrary form.We provide sufficient conditions for point identification.We then propose an estimator and show that it is √n-consistent and asymptotically normal. We conduct a simulation study that reveals adequate finite sample performance. We also use the estimator in an empirical illustration of export duration, where we find advantages of the proposed method over existing ones. © 2011 American Statistical Association.

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http://hdl.handle.net/10453/112683