Semi-parametric inference for semi-varying coefficient panel data model with individual effects
Hu, Xuemei1,2
刊名JOURNAL OF MULTIVARIATE ANALYSIS
2017-02-01
卷号154页码:262-281
关键词Panel data Fixed effects Random effects Local linear smoothing Semi-varying coefficient model Bootstrap procedure
ISSN号0047-259X
DOI10.1016/j.jmva.2016.11.007
英文摘要We study a semi-varying coefficient panel data model with unobserved individual effects, where all the covariates are high-dimensional variables. Based on multivariate local linear fitting, the transformation technique and the profile likelihood method, we establish semi parametric fixed effects estimators, semi-parametric random effects estimators, and their asymptotic properties. We also introduce a test for discriminating between a semi-varying coefficient random effects panel data model and a semi-varying coefficient fixed effects panel data model. The critical values are estimated by a bootstrap procedure. Monte Carlo studies exhibit the finite-sample performance of the proposed estimators and test statistics. Simulation results show that the methods perform well for moderate sample sizes. Finally, we analyze the cigarette consumption panel data from 46 American states covering the period 1963-1992. (C) 2016 Elsevier Inc. All rights reserved.
语种英语
出版者ELSEVIER INC
WOS记录号WOS:000391907200016
内容类型期刊论文
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/24540]  
专题中国科学院数学与系统科学研究院
通讯作者Hu, Xuemei
作者单位1.Chongqing Technol & Business Univ, Sch Math & Stat, Chongqing 400067, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714
Hu, Xuemei. Semi-parametric inference for semi-varying coefficient panel data model with individual effects[J]. JOURNAL OF MULTIVARIATE ANALYSIS,2017,154:262-281.
APA Hu, Xuemei.(2017).Semi-parametric inference for semi-varying coefficient panel data model with individual effects.JOURNAL OF MULTIVARIATE ANALYSIS,154,262-281.
MLA Hu, Xuemei."Semi-parametric inference for semi-varying coefficient panel data model with individual effects".JOURNAL OF MULTIVARIATE ANALYSIS 154(2017):262-281.
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