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Statistical inference in partially time-varying coefficient models

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Statistical inference in partially time-varying coefficient models. / Li, Degui; Chen, Jia; Lin, Zhengyan.

In: Journal of Statistical Planning and Inference, Vol. 141, No. 2, 21.02.2011, p. 995-1013.

Research output: Contribution to journalArticlepeer-review

Harvard

Li, D, Chen, J & Lin, Z 2011, 'Statistical inference in partially time-varying coefficient models', Journal of Statistical Planning and Inference, vol. 141, no. 2, pp. 995-1013. https://doi.org/10.1016/j.jspi.2010.09.004

APA

Li, D., Chen, J., & Lin, Z. (2011). Statistical inference in partially time-varying coefficient models. Journal of Statistical Planning and Inference, 141(2), 995-1013. https://doi.org/10.1016/j.jspi.2010.09.004

Vancouver

Li D, Chen J, Lin Z. Statistical inference in partially time-varying coefficient models. Journal of Statistical Planning and Inference. 2011 Feb 21;141(2):995-1013. https://doi.org/10.1016/j.jspi.2010.09.004

Author

Li, Degui ; Chen, Jia ; Lin, Zhengyan. / Statistical inference in partially time-varying coefficient models. In: Journal of Statistical Planning and Inference. 2011 ; Vol. 141, No. 2. pp. 995-1013.

Bibtex - Download

@article{b8c7b4b491a14bc9b135ff09285dafce,
title = "Statistical inference in partially time-varying coefficient models",
abstract = "A partially time-varying coefficient time series model is introduced to characterize the nonlinearity and trending phenomenon. To estimate the regression parameter and the nonlinear coefficient function, the profile least squares approach is applied with the help of local linear approximation. The asymptotic distributions of the proposed estimators are established under mild conditions. Meanwhile, the generalized likelihood ratio test is studied and the test statistics are demonstrated to follow asymptotic chi(2)-distribution under the null hypothesis. Furthermore, some extensions of the proposed model are discussed and several numerical examples are provided to illustrate the finite sample behavior of the proposed methods.",
keywords = "Local linear smoother, Generalized likelihood ratio statistics, PARTIALLY LINEAR-MODELS, Profile least squares, Time-varying coefficient model, ERRORS, SERIES MODELS, SEMIPARAMETRIC REGRESSION, CENTRAL-LIMIT-THEOREM, VARIABLE SELECTION, Semiparametric regression",
author = "Degui Li and Jia Chen and Zhengyan Lin",
note = " (C) 2010 Elsevier B.V. All rights reserved.",
year = "2011",
month = feb,
day = "21",
doi = "10.1016/j.jspi.2010.09.004",
language = "English",
volume = "141",
pages = "995--1013",
journal = "Journal of Statistical Planning and Inference",
issn = "0378-3758",
publisher = "Elsevier",
number = "2",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Statistical inference in partially time-varying coefficient models

AU - Li, Degui

AU - Chen, Jia

AU - Lin, Zhengyan

N1 - (C) 2010 Elsevier B.V. All rights reserved.

PY - 2011/2/21

Y1 - 2011/2/21

N2 - A partially time-varying coefficient time series model is introduced to characterize the nonlinearity and trending phenomenon. To estimate the regression parameter and the nonlinear coefficient function, the profile least squares approach is applied with the help of local linear approximation. The asymptotic distributions of the proposed estimators are established under mild conditions. Meanwhile, the generalized likelihood ratio test is studied and the test statistics are demonstrated to follow asymptotic chi(2)-distribution under the null hypothesis. Furthermore, some extensions of the proposed model are discussed and several numerical examples are provided to illustrate the finite sample behavior of the proposed methods.

AB - A partially time-varying coefficient time series model is introduced to characterize the nonlinearity and trending phenomenon. To estimate the regression parameter and the nonlinear coefficient function, the profile least squares approach is applied with the help of local linear approximation. The asymptotic distributions of the proposed estimators are established under mild conditions. Meanwhile, the generalized likelihood ratio test is studied and the test statistics are demonstrated to follow asymptotic chi(2)-distribution under the null hypothesis. Furthermore, some extensions of the proposed model are discussed and several numerical examples are provided to illustrate the finite sample behavior of the proposed methods.

KW - Local linear smoother

KW - Generalized likelihood ratio statistics

KW - PARTIALLY LINEAR-MODELS

KW - Profile least squares

KW - Time-varying coefficient model

KW - ERRORS

KW - SERIES MODELS

KW - SEMIPARAMETRIC REGRESSION

KW - CENTRAL-LIMIT-THEOREM

KW - VARIABLE SELECTION

KW - Semiparametric regression

UR - http://www.scopus.com/inward/record.url?scp=77956592418&partnerID=8YFLogxK

U2 - 10.1016/j.jspi.2010.09.004

DO - 10.1016/j.jspi.2010.09.004

M3 - Article

VL - 141

SP - 995

EP - 1013

JO - Journal of Statistical Planning and Inference

JF - Journal of Statistical Planning and Inference

SN - 0378-3758

IS - 2

ER -