INFERENCE OF TIME-VARYING REGRESSION MODELS

• 成果类型: Article
• 署名作者: Zhang, Ting; Wu, Wei Biao
• 署名单位: University of Iowa; University of Chicago
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/12-AOS1010
• 发表日期: 2012
• 页码: 1376-1402
• 关键词: consistent covariance-matrix; coefficient models; longitudinal data; series models; Nonparametric Regression; AUTOREGRESSIVE PROCESSES; linear-models; stationary-processes; efficient estimation; specification tests

摘要

We consider parameter estimation, hypothesis testing and variable selection for partially time-varying coefficient models. Our asymptotic theory has the useful feature that it can allow dependent, nonstationary error and covariate processes. With a two-stage method, the parametric component can be estimated with a n(1/2)-convergence rate. A simulation-assisted hypothesis testing procedure is proposed for testing significance and parameter constancy. We further propose an information criterion that can consistently select the true set of significant predictors. Our method is applied to autoregressive models with time-varying coefficients. Simulation results and a real data application are provided.