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.
| Original language | English |
|---|---|
| Pages (from-to) | 995-1013 |
| Number of pages | 19 |
| Journal | Journal of Statistical Planning and Inference |
| Volume | 141 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 21 Feb 2011 |
Bibliographical note
(C) 2010 Elsevier B.V. All rights reserved.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