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 I?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.
Li, D., Chen, J., & Lin, Z. (2011). Statistical inference in partially time-varying coefficient models. Journal of Statistical Planning and Inference, 141(2), 995 - 1013. https://doi.org/10.1016/j.jspi.2010.09.004