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Abstract Details
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Activity Number:
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406
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2012 : 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 - #304782 |
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Title:
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Time Series Goodness-of-Fit Testing Using a Weighted Portmanteau Statistic
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Author(s):
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Thomas Fisher*+ and Colin Gallagher
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Companies:
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University of Missouri-Kansas City and Clemson University
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Address:
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Dept. of Math and Statistics, Kansas City, MO, 64110, United States
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Keywords:
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Time Series ;
ARMA Process ;
GARCH process ;
Goodness-of-fit
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Abstract:
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A new portmanteau statistic for time series goodness-of-fit is derived using methodology from high-dimensional multivariate analysis. The resulting test is a weighted sum of the squares of sample autocorrelations of the residuals from a fitted autoregressive-moving average process. The proposed statistic is asymptotically distributed as a linear combination of chi square random variables and the distribution can be approximated with a gamma distribution. The proposed method is easy to implement and simulation results indicate the proposed method is more powerful than the traditional methods. The methodology can be extended for detecting and diagnosing the fit of nonlinear models.
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