Activity Number:
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241
- Estimation Challenges and New Approaches
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Type:
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Contributed
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Date/Time:
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Monday, July 29, 2019 : 2:00 PM to 3:50 PM
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Sponsor:
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Business and Economic Statistics Section
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Abstract #304174
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Title:
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Forecast Combination Using High-Dimensional Precision Matrix
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Author(s):
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Tae-Hwy Lee* and Yi Millie Mao and Aman Ullah
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Companies:
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Univ of California, Riverside and University of California, Riverside and University of California, Riverside
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Keywords:
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High-dimensional precision matrix;
ISEE;
Forecast combination puzzle
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Abstract:
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The estimation of a large covariance matrix is challenging when the dimension $p$ is large relative to the sample size $n$. Common approaches to deal with the challenge have been based on thresholding or shrinkage methods in estimating large covariance matrices. We examine the combined forecasts based on the ISEE estimator of Fan and Lv (2016) and compare it with those based on the thresholding and shrinkage methods.
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Authors who are presenting talks have a * after their name.