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Abstract Details
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Activity Number:
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360
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Business and Economic Statistics Section
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| Abstract - #301270 |
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Title:
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Sieve Bootstrap Prediction Intervals for Multivariate ARMA Processes with Non-Gaussian Innovations
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Author(s):
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Purna Mukhopadhyay*+ and V. A. Samaranayake
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Companies:
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University of Kansas and Missouri university of Science and Technology
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Address:
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Medical Center, Overland Park, KS, 66209,
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Keywords:
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Sieve Bootstrap ;
Multivariate ;
ARMA processes ;
non-Gaussian ;
nonparametric
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Abstract:
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Existing nonparametric bootstrap methods for obtaining prediction intervals for vector autoregressive moving average (ARMA) processes require apriori knowledge of the autoregressive and moving average orders, p, q respectively. The sieve bootstrap method developed for stationary and invertible univariate processes overcomes this limitation. We implement a modified version of the sieve bootstrap method to multivariate ARMA processes and show, through a Monte Carlo study, that the procedure produces prediction intervals that achieve nominal or near nominal coverage probabilities. The robustness of this method under non-normality is tested using different error distributions and Monte Carlo results show that the coverage remains at nominal or near nominal levels under several non-normal distributions.
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