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Estimation of Semi-Varying Coefficient Models with Nonstationary Regressors

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Author(s)

  • Kunpeng Li
  • Degui Li
  • Zhongwen Liang
  • Cheng Hsiao

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Publication details

JournalEconometric Reviews
DateAccepted/In press - 7 Nov 2015
DateE-pub ahead of print - 7 Nov 2015
DatePublished (current) - 2017
Issue number1-3
Volume36
Number of pages16
Pages (from-to)354-369
Early online date7/11/15
Original languageEnglish

Abstract

We study a semi-varying coefficient model where the regressors are generated by the multivariate unit root I(1) processes. The influence of the explanatory vectors on the response variable satisfies the semiparametric partially linear structure with the nonlinear component being functional coefficients. A semiparametric estimation methodology with the first-stage local polynomial smoothing is applied to estimate both the constant coefficients in the linear component and the functional coefficients in the nonlinear component. The asymptotic distribution theory for the proposed semiparametric estimators is established under some mild conditions, from which both the parametric and nonparametric estimators are shown to enjoy the well-known super-consistency property. Furthermore, a simulation study is conducted to investigate the finite sample performance of the developed methodology and results.

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© 2017 Taylor & Francis Group, LLC. This is an author produced version of a paper accepted for publication in Econometric Reviews. Uploaded in accordance with the publisher's self-archiving policy.

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