Statistical inference in partially time-varying coefficient models |
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| Authors: | Degui Li Jia Chen Zhengyan Lin |
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| Institution: | a Department of Mathematics, Zhejiang University, Hangzhou 310027, China
b School of Economics, The University of Adelaide, Adelaide, SA 5005, Australia |
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| 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 χ2-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. |
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| Keywords: | Generalized likelihood ratio statistics Local linear smoother Profile least squares Semiparametric regression Time-varying coefficient model |
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