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Abstract Details
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Activity Number:
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31
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Type:
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Contributed
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Date/Time:
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Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #301142 |
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Title:
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Nonparametric Estimation of Innovation Variance: A Case Study on the Wolfer's Sunspot Numbers
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Author(s):
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Priya Kohli*+ and Mohsen Pourahmadi
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Companies:
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Texas A & M University and Texas A & M University
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Address:
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Department of Statistics, Texas A&M University, College Station, TX, 77843-3143, US
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Keywords:
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Innovation Variance ;
Periodogram ;
Tapering ;
Spectral density
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Abstract:
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For a stationary time series nonparametric estimators of the innovation variance can provide useful information to judge the fit of various parametric models. We review the progress in nonparametric estimation of the innovation(one-step ahead prediction error)variance that started with the work of Davis and Jones (1968) using the Szego-Kolmogorov formula for the innovation variance and replacing the unknown spectral density by the periodogram and/or its smoothed/tapered version. The approximate finite-sample distribution of the estimator and its performance is assessed. Similar nonparametric methods are proposed for time series with missing values. We illustrate the role of the nonparametric estimator in judging the fit of numerous parametric models fitted in the last eight decades to the well-known Wolfer's sunspot numbers.
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