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Local Polynomial Fitting in Semivarying Coefficient Model

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  • Zhang, Wenyang
  • Lee, Sik-Yum
  • Song, Xinyuan
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    Abstract

    Varying coefficient models are useful extensions of the classical linear models. Under the condition that the coefficient functions possess about the same degrees of smoothness, the model can easily be estimated via simple local regression. This leads to the one-step estimation procedure. In this paper, we consider a semivarying coefficient model which is an extension of the varying coefficient model, which is called the semivarying-coefficient model. Procedures for estimation of the linear part and the nonparametric part are developed and their associated statistical properties are studied. The proposed methods are illustrated by some simulation studies and a real example.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 82 (2002)
    Issue (Month): 1 (July)
    Pages: 166-188

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    Handle: RePEc:eee:jmvana:v:82:y:2002:i:1:p:166-188

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    Related research

    Keywords: semivarying-coefficient models varying-coefficient models local polynomial fit one-step method two-step method optimal rate of convergence mean squared errors;

    References

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    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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    1. Zhang, Wenyang & Lee, Sik-Yum, 2000. "Variable Bandwidth Selection in Varying-Coefficient Models," Journal of Multivariate Analysis, Elsevier, vol. 74(1), pages 116-134, July.
    2. Zongwu Cai & Jianqing Fan & Qiwei Yao, 2000. "Functional-coefficient regression models for nonlinear time series," LSE Research Online Documents on Economics 6314, London School of Economics and Political Science, LSE Library.
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    Citations

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    Cited by:
    1. Zongwu Cai & Zhijie Xiao, 2010. "Semiparametric Quantile Regression Estimation in Dynamic Models with Partially Varying Coefficients," Boston College Working Papers in Economics 761, Boston College Department of Economics.
    2. Y. Andriyana & I. Gijbels & A. Verhasselt, 2014. "P-splines quantile regression estimation in varying coefficient models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 23(1), pages 153-194, March.
    3. Delis, Manthos D & Kokas, Sotiris, 2014. "Foreign ownership and market power in banking: Evidence from a world sample," MPRA Paper 53957, University Library of Munich, Germany.
    4. Clifford Lam & Jianqing Fan, 2008. "Profile-kernel likelihood inference with diverging number of parameters," LSE Research Online Documents on Economics 31548, London School of Economics and Political Science, LSE Library.
    5. Shen, Si-Lian & Cui, Jian-Ling & Mei, Chang-Lin & Wang, Chun-Wei, 2014. "Estimation and inference of semi-varying coefficient models with heteroscedastic errors," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 70-93.
    6. Weihua Zhao & Riquan Zhang & Jicai Liu & Yazhao Lv, 2014. "Robust and efficient variable selection for semiparametric partially linear varying coefficient model based on modal regression," Annals of the Institute of Statistical Mathematics, Springer, vol. 66(1), pages 165-191, February.
    7. Delis, Manthos D & Iosifidi, Maria & Tsionas, Efthymios, 2012. "On the estimation of marginal cost," MPRA Paper 43514, University Library of Munich, Germany.
    8. Cai, Zongwu & Das, Mitali & Xiong, Huaiyu & Wu, Xizhi, 2006. "Functional coefficient instrumental variables models," Journal of Econometrics, Elsevier, vol. 133(1), pages 207-241, July.
    9. Shang, Suoping & Zou, Changliang & Wang, Zhaojun, 2012. "Local Walsh-average regression for semiparametric varying-coefficient models," Statistics & Probability Letters, Elsevier, vol. 82(10), pages 1815-1822.
    10. Huang, Zhensheng & Zhang, Riquan, 2009. "Efficient estimation of adaptive varying-coefficient partially linear regression model," Statistics & Probability Letters, Elsevier, vol. 79(7), pages 943-952, April.
    11. Yang, Hu & Li, Tingting, 2010. "Empirical likelihood for semiparametric varying coefficient partially linear models with longitudinal data," Statistics & Probability Letters, Elsevier, vol. 80(2), pages 111-121, January.
    12. Pantelis Kalaitzidakis. & Theofanis P. Mamuneas. & Thanasis Stengos., 2008. "The Contribution of Greenhouse Pollution to Productivity Growth," Working Papers 0802, University of Guelph, Department of Economics and Finance.
    13. Senturk, Damla & Nguyen, Danh V., 2006. "Estimation in covariate-adjusted regression," Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 3294-3310, July.
    14. Wong, Heung & Ip, Wai-cheung & Zhang, Riquan, 2008. "Varying-coefficient single-index model," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1458-1476, January.
    15. repec:wyi:wpaper:001967 is not listed on IDEAS
    16. Neophyta Empora & Theofanis Mamuneas, 2011. "The Effect of Emissions on U.S. State Total Factor Productivity Growth," Review of Economic Analysis, Rimini Centre for Economic Analysis, vol. 3(2), pages 149-172, October.

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