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Activity Number: 270 - Advanced Multivariate Time Series Modeling
Type: Topic Contributed
Date/Time: Tuesday, August 9, 2022 : 10:30 AM to 12:20 PM
Sponsor: International Chinese Statistical Association
Abstract #320886
Title: A Stratified Penalization Method for Semiparametric Variable Labeling of Multi-Output, Time-Varying Coefficient Models
Author(s): Ting Zhang and Weiliang Wang* and Yu Shao
Companies: University of Georgia and Boston University and Boston University
Keywords: kernel smoothing; local linear estimation; nonstationary time series; time-varying coefficient model; variable selection
Abstract:

In a time-varying coefficient model, the regression coefficient is allowed to change over time as a nonparametric function to capture the time-varying feature. We consider the problem of semiparametric variable labeling and estimation for multi-output time-varying coefficient models in the time series setting, where a variable can be labeled as time-varying, time-constant, or irrelevant, in a nested structure. We first show that the natural approach of imposing separate penalties on the local linear estimator and its derivative will not work as intended for semiparametric labeling due to the lack of connection between the coefficient and derivative estimators in the popular local linear method. We then propose a stratified fix that borrows information from the coefficient estimator and puts together with the derivative into the same stratum that achieves successful labeling and estimation at the same time. Theoretical properties of the proposed method, including its estimation and labeling consistency, are established for a general class of nonstationary processes. A Monte Carlo simulation study and a real data application are presented to further illustrate the proposed method.


Authors who are presenting talks have a * after their name.

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