Semiparametric Profile Likelihood Estimation of Varying Coefficient Models with Nonstationary Regressors

Kunpeng Li (), Degui Li, Zhongwen Lian () and Cheng Hsiao
Authors registered in the RePEc Author Service: Zhongwen Liang ()

No 2/13, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: We study a partially linear 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. The profile likelihood 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.

Keywords: Functional coefficients; local polynomial fitting; profile likelihood; semiparametric estimation; unit root process. (search for similar items in EconPapers)
Date: 2013
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (1)

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