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Title: Asymptotic theory for time-varying regression models Authors:  Wei Biao Wu - University of Chicago (United States) [presenting]
Abstract: Parameter estimation, hypothesis testing and variable selection is considered 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 root $n$-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.