Inference of time-varying regression models
The Annals of Statistics · 2012 · Volume 40 · Issue 3
Abstract
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.
Keywords
62G05, 62G10, 62G20, information criterion, Locally stationary processes, nonparametric hypothesis testings, time-varying coefficient models, Variable selection
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