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Activity Number:
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425
- Nonparametric Methods for Dependent Data
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Type:
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Contributed
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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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Section on Nonparametric Statistics
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Abstract #322458
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Title:
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Sample Splitting Based Inference for Multi-Dimensional Parameter in Time Series
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Author(s):
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Yi Zhang* and Xiaofeng Shao
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Companies:
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University of Illinois at Urbana-Champaign and University of Illinois at Urbana-Champaign
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Keywords:
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Self-normalization;
time series
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Abstract:
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The traditional testing procedure for the null hypothesis about a multi-dimensional parameter in a stationary time series requires a consistent estimator of the long run covariance matrix. The commonly used estimator needs to choose a bandwidth parameter and is difficult to implement in practice. The self-normalization (SN) method avoids the bandwidth choice, is asymptotically distribution-free under the null but the size distortion is serious when the dimension is moderate and the temporal dependence is strong. In this talk, I will present a sample splitting based approach to testing and show the favorable size performance through simulations.
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Authors who are presenting talks have a * after their name.
