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