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	Identification and estimation of partially linear censored regression models with unknown heteroscedasticity
	Zhang, Zhengyu; Liu, Bing
	2015-06
关键词	Fixed censoring Heteroscedasticity Partially linear model Quantile regression Random censoring
卷号	18
期号	2
DOI	10.1111/ectj.12037
页码	242-273
英文摘要	In this paper, we introduce a new identification and estimation strategy for partially linear regression models with a general form of unknown heteroscedasticity, that is, Y=X0+m(Z)+U and U=sigma(X,Z)epsilon, where epsilon is independent of (X,Z) and the functional forms of both m() and sigma() are left unspecified. We show that in such a model, (0) and m() can be exactly identified while sigma() can be identified up to scale as long as sigma(X,Z) permits sufficient nonlinearity in X. A two-stage estimation procedure motivated by the identification strategy is described and its large sample properties are formally established. Moreover, our strategy is flexible enough to allow for both fixed and random censoring in the dependent variable. Simulation results show that the proposed estimator performs reasonably well in finite samples.
会议录出版者	WILEY-BLACKWELL
会议录出版地	111 RIVER ST, HOBOKEN 07030-5774, NJ USA
语种	英语
WOS研究方向	Business & Economics ; Mathematics ; Mathematical Methods In Social Sciences
WOS记录号	WOS:000357951800008
内容类型	会议论文
源URL	[http://10.2.47.112/handle/2XS4QKH4/3380]
专题	上海财经大学
作者单位	Shanghai Univ Finance & Econ, Sch Econ, Shanghai 200433, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Zhengyu,Liu, Bing. Identification and estimation of partially linear censored regression models with unknown heteroscedasticity[C]. 见:.

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