| Content | This paper considers estimation and inference for varying-coefficient models with
nonstationary regressors. We propose a nonparametric estimation method using penalized
splines, which achieves the same optimal convergence rate as kernel-based
methods, but enjoys computation advantages. Utilizing the mixed model representation
of penalized splines, we develop a likelihood ratio test statistic for checking the
stability of the regression coefficients. We derive both the exact and the asymptotic
null distributions of this test statistic. We also demonstrate its optimality by examining
its local power performance. These theoretical findings are well supported by
simulation studies. |
