Estimation and Inference for Varying-coefficient Models with Nonstationary Regressors using Penalized Splines
Haiqiang Chen, Ying Fang, Yingxing Li
#002195 20131014 (published) Views:128
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 includings are well supported by simulation studies.
Keywords: Nonstationary Time Series; Varying-coefficient Model; Likelihood Ratio Test; Penalized Splines