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Activity Number:
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444
- Recent Advances in Statistical Methodology for Big Data
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Type:
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Contributed
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Date/Time:
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Thursday, August 12, 2021 : 4:00 PM to 5:50 PM
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Sponsor:
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IMS
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Abstract #318243
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Title:
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Simultaneous Inference for the Common and Idiosyncratic of the Dynamic Factor Model
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Author(s):
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Yuanyuan Zhang* and Jiangyan Wang and Xinbing Kong
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Companies:
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Soochow University and Nanjing Audit University and Nanjing Audit University
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Keywords:
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Dynamic factor model;
Empirical process;
Dynamic principal component;
Simultaneous confidence band
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Abstract:
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The dynamic principal component method can estimate the common and idiosyncratic components consistently. Based on the estimated common and idiosyncratic components, two empirical processes for distribution functions of the common and idiosyncratic components are constructed, respectively. We prove that these two empirical processes are oracle efficient when T=o(n) where n is the dimension and and T is the sample size. This validates that the decomposition of individual variables to common and idiosyncratic empirical processes on the dynamic factor model. Based on this oracle property, we construct simultaneous confidence bands (SCBs) for the distributions of the common and idiosyncratic components. Simulation and empirical studies verify that the estimated bands perform well in terms of coverage frequencies.
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Authors who are presenting talks have a * after their name.
