This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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124
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 2, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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Business and Economic Statistics Section
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Abstract - #307388 |
Title:
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An Evaluation of Kan and Wang's Adjusted Box-Pierce Test Using Seasonal Time Series
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Author(s):
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Brian Carl Monsell*+
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Companies:
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U.S. Census Bureau
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Address:
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Statistical Research Division, Washington, DC, 20233,
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Keywords:
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Autocorrelation ;
regARIMA model ;
Kalman filter ;
modeling diagnostics ;
elliptical distributions
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Abstract:
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Kan and Wang (2010) develop an exact distribution for the sample autocorrelation coefficients. Using this distribution, the authors propose an adjustment to the Box-Pierce diagnostic commonly used to test the randomness of time series, and show it has superior size properties to the traditional Box-Pierce and Ljung-Box statistics. In this paper, I will examine the size and power properties of this new diagnostic for seasonal lags, and will examine the performance of the adjusted diagnostic versus the Box-Pierce and Ljung-Box statistics for simulated and Census Bureau time series. Also, Ansley and Newbold (1979) showed that Box-Pierce or Ljung-Box statistics computed from autocorrelations of Kalman filter innovations had better properties than those derived from the residuals of MLE for ARMA models. I will seek to confirm this, and compare such statistics to the Kan and Wang statistic.
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Authors who are presenting talks have a * after their name.
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