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Abstract Details
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Activity Number:
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246
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Type:
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Contributed
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Date/Time:
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Monday, August 1, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Committee on Applied Statisticians
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| Abstract - #301045 |
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Title:
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Inference for Nonstationary Time Series
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Author(s):
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Xiaoye Li*+
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Companies:
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Penn State University
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Address:
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126 E Nittany Ave. Apt. 4, State College, PA, 16801, USA
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Keywords:
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Change-point ;
Confidence interval ;
Strong invariance principle ;
Long-run variance ;
Non-stationary time series ;
Self-normalize
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Abstract:
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We study statistical inference for a class of non-stationary time series with time dependent variances. Based on a self-normalization technique, we address several inference problems, including self-normalized Central Limit Theorem, self-normalized cumulative sum test for change-point problem, long-run variance estimation through blockwise self-normalization, and self-normalization based wild bootstrap for non-stationary time series. Monte Carlo simulation studies show that the proposed self-normalization based methods outperform stationarity based alternatives. We demonstrate the proposed methodology using two real data sets: annual mean precipitation rates in Seoul during 1771-2000, and quarterly U.S. Gross National Product growth rates during 1947-2002.
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Authors who are presenting talks have a * after their name.
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