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Abstract Details
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Activity Number:
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467
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #301537 |
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Title:
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Boosting Threshold Autoregressive Models
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Author(s):
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Qing Mai*+ and Hui Zou
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Companies:
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University of Minnesota and University of Minnesota
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Address:
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313 Ford Hall, Minneapolis, MN, 55455,
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Keywords:
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Threshold autoregressive model ;
Boosting
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Abstract:
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Due to its interpretability, threshold autoregressive models(TAR) are widely used in economics, epidemiology, sociology, psychology and other fields. However, the current method of estimation for TAR involves assuming a certain form of the threshold and searching for an estimate exhaustively. Hence, the estimation is vulnerable to model misspecification and is expensive in computation. We propose to employ boosting to estimate TAR models. The proposed method does not require assumptions on the form of the threshold and is computationally efficient. Numerical studies will be presented to illustrate the performance of the new method.
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