Semi-parametric inference for semi-varying coefficient panel data model with individual effects

JOURNAL OF MULTIVARIATE ANALYSIS

卷:154

Article

Hu, Xuemei^1,2 

[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

关键词: Panel data;    Fixed effects;    Random effects;    Local linear smoothing;    Semi-varying coefficient model;    Bootstrap procedure;   

DOI  :  10.1016/j.jmva.2016.11.007

来源: Elsevier

【 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.

【 授权许可】

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