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Activity Number: 397 - Modern Statistical Learning Methods for Dynamic Models
Type: Invited
Date/Time: Wednesday, August 10, 2022 : 10:30 AM to 12:20 PM
Sponsor: Business and Economic Statistics Section
Abstract #320558
Title: Simultaneous Inference for Time-Varying Models
Author(s): Wei Biao Wu and Sayar Karmakar*
Companies: University of Chicago and University of Florida
Keywords: time-varying regression
Abstract:

We consider a general class of time-varying regression models and estimate the regression coefficients by using local linear M-estimation. For these estimators, weak Bahadur representations are obtained and are used to construct simultaneous confidence bands. For practical implementation, we propose a bootstrap based method to circumvent the slow logarithmic convergence of the theoretical simultaneous bands. Our results substantially generalize and unify the treatments for several time-varying regression and auto-regression models. The performance for ARCH and GARCH models is studied in simulations and a few real-life applications of our study are presented through analysis of some popular financial datasets. The work is joint with Sayar Karmakar and Stefan Richter.


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